id	sid	tid	token	lemma	pos
fcis-9856	1	1	frontiers	frontier	NOUN
fcis-9856	1	2	in	in	ADP
fcis-9856	1	3	computing	computing	NOUN
fcis-9856	1	4	and	and	CCONJ
fcis-9856	1	5	intelligent	intelligent	ADJ
fcis-9856	1	6	systems	system	NOUN
fcis-9856	1	7	issn	issn	VERB
fcis-9856	1	8	:	:	PUNCT
fcis-9856	1	9	2832	2832	NUM
fcis-9856	1	10	-	-	SYM
fcis-9856	1	11	6024	6024	NUM
fcis-9856	1	12	|	|	NOUN
fcis-9856	1	13	vol	vol	NOUN
fcis-9856	1	14	.	.	PROPN
fcis-9856	2	1	4	4	NUM
fcis-9856	2	2	,	,	PUNCT
fcis-9856	2	3	no	no	INTJ
fcis-9856	2	4	.	.	NOUN
fcis-9856	2	5	2	2	NUM
fcis-9856	2	6	,	,	PUNCT
fcis-9856	2	7	2023	2023	NUM
fcis-9856	2	8	31	31	NUM
fcis-9856	2	9	a	a	DET
fcis-9856	2	10	super‐resolution	super‐resolution	NOUN
fcis-9856	2	11	algorithm	algorithm	NOUN
fcis-9856	2	12	for	for	ADP
fcis-9856	2	13	remote	remote	ADJ
fcis-9856	2	14	sensing	sensing	NOUN
fcis-9856	2	15	images	image	NOUN
fcis-9856	2	16	based	base	VERB
fcis-9856	2	17	on	on	ADP
fcis-9856	2	18	data	data	NOUN
fcis-9856	2	19	enhancement	enhancement	NOUN
fcis-9856	2	20	and	and	CCONJ
fcis-9856	2	21	generative	generative	ADJ
fcis-9856	2	22	adversarial	adversarial	ADJ
fcis-9856	2	23	networks	network	NOUN
fcis-9856	2	24	wei	wei	PROPN
fcis-9856	2	25	chen	chen	PROPN
fcis-9856	2	26	,	,	PUNCT
fcis-9856	2	27	haiwei	haiwei	PROPN
fcis-9856	2	28	shen	shen	PROPN
fcis-9856	3	1	*	*	PUNCT
fcis-9856	3	2	college	college	NOUN
fcis-9856	3	3	of	of	ADP
fcis-9856	3	4	applied	apply	VERB
fcis-9856	3	5	science	science	NOUN
fcis-9856	3	6	and	and	CCONJ
fcis-9856	3	7	technology	technology	NOUN
fcis-9856	3	8	of	of	ADP
fcis-9856	3	9	beijing	beijing	PROPN
fcis-9856	3	10	union	union	PROPN
fcis-9856	3	11	university	university	PROPN
fcis-9856	3	12	,	,	PUNCT
fcis-9856	3	13	beijing	beijing	PROPN
fcis-9856	3	14	,	,	PUNCT
fcis-9856	3	15	china	china	PROPN
fcis-9856	3	16	*	*	PUNCT
fcis-9856	3	17	corresponding	correspond	VERB
fcis-9856	3	18	author	author	NOUN
fcis-9856	3	19	:	:	PUNCT
fcis-9856	3	20	haiwei	haiwei	PROPN
fcis-9856	3	21	shen	shen	PROPN
fcis-9856	3	22	abstract	abstract	PROPN
fcis-9856	3	23	:	:	PUNCT
fcis-9856	3	24	in	in	ADP
fcis-9856	3	25	this	this	DET
fcis-9856	3	26	paper	paper	NOUN
fcis-9856	3	27	,	,	PUNCT
fcis-9856	3	28	a	a	DET
fcis-9856	3	29	new	new	ADJ
fcis-9856	3	30	algorithm	algorithm	NOUN
fcis-9856	3	31	is	be	AUX
fcis-9856	3	32	proposed	propose	VERB
fcis-9856	3	33	to	to	PART
fcis-9856	3	34	address	address	VERB
fcis-9856	3	35	the	the	DET
fcis-9856	3	36	problem	problem	NOUN
fcis-9856	3	37	of	of	ADP
fcis-9856	3	38	excessive	excessive	ADJ
fcis-9856	3	39	errors	error	NOUN
fcis-9856	3	40	in	in	ADP
fcis-9856	3	41	current	current	ADJ
fcis-9856	3	42	super	super	ADJ
fcis-9856	3	43	-	-	ADJ
fcis-9856	3	44	resolution	resolution	ADJ
fcis-9856	3	45	algorithms	algorithm	NOUN
fcis-9856	3	46	for	for	ADP
fcis-9856	3	47	remote	remote	ADJ
fcis-9856	3	48	sensing	sensing	NOUN
fcis-9856	3	49	images	image	NOUN
fcis-9856	3	50	,	,	PUNCT
fcis-9856	3	51	which	which	PRON
fcis-9856	3	52	optimizes	optimize	VERB
fcis-9856	3	53	the	the	DET
fcis-9856	3	54	processing	processing	NOUN
fcis-9856	3	55	effect	effect	NOUN
fcis-9856	3	56	of	of	ADP
fcis-9856	3	57	generative	generative	ADJ
fcis-9856	3	58	adversarial	adversarial	ADJ
fcis-9856	3	59	networks	network	NOUN
fcis-9856	3	60	by	by	ADP
fcis-9856	3	61	increasing	increase	VERB
fcis-9856	3	62	the	the	DET
fcis-9856	3	63	type	type	NOUN
fcis-9856	3	64	and	and	CCONJ
fcis-9856	3	65	size	size	NOUN
fcis-9856	3	66	of	of	ADP
fcis-9856	3	67	training	training	NOUN
fcis-9856	3	68	data	datum	NOUN
fcis-9856	3	69	with	with	ADP
fcis-9856	3	70	data	datum	NOUN
fcis-9856	3	71	enhancement	enhancement	NOUN
fcis-9856	3	72	techniques	technique	NOUN
fcis-9856	3	73	.	.	PUNCT
fcis-9856	4	1	first	first	ADV
fcis-9856	4	2	,	,	PUNCT
fcis-9856	4	3	the	the	DET
fcis-9856	4	4	algorithm	algorithm	NOUN
fcis-9856	4	5	constructs	construct	VERB
fcis-9856	4	6	a	a	DET
fcis-9856	4	7	more	more	ADV
fcis-9856	4	8	reasonable	reasonable	ADJ
fcis-9856	4	9	degradation	degradation	NOUN
fcis-9856	4	10	model	model	NOUN
fcis-9856	4	11	for	for	ADP
fcis-9856	4	12	the	the	DET
fcis-9856	4	13	problem	problem	NOUN
fcis-9856	4	14	of	of	ADP
fcis-9856	4	15	lacking	lack	VERB
fcis-9856	4	16	data	data	NOUN
fcis-9856	4	17	sets	set	NOUN
fcis-9856	4	18	of	of	ADP
fcis-9856	4	19	paired	pair	VERB
fcis-9856	4	20	images	image	NOUN
fcis-9856	4	21	,	,	PUNCT
fcis-9856	4	22	and	and	CCONJ
fcis-9856	4	23	randomly	randomly	ADV
fcis-9856	4	24	mixes	mix	NOUN
fcis-9856	4	25	and	and	CCONJ
fcis-9856	4	26	washes	wash	VERB
fcis-9856	4	27	the	the	DET
fcis-9856	4	28	degraded	degraded	ADJ
fcis-9856	4	29	prior	prior	ADJ
fcis-9856	4	30	knowledge	knowledge	NOUN
fcis-9856	4	31	(	(	PUNCT
fcis-9856	4	32	such	such	ADJ
fcis-9856	4	33	as	as	ADP
fcis-9856	4	34	blur	blur	NOUN
fcis-9856	4	35	,	,	PUNCT
fcis-9856	4	36	noise	noise	NOUN
fcis-9856	4	37	,	,	PUNCT
fcis-9856	4	38	downsampling	downsampling	NOUN
fcis-9856	4	39	,	,	PUNCT
fcis-9856	4	40	etc	etc	X
fcis-9856	4	41	.	.	X
fcis-9856	4	42	)	)	PUNCT
fcis-9856	4	43	in	in	ADP
fcis-9856	4	44	the	the	DET
fcis-9856	4	45	imaging	imaging	NOUN
fcis-9856	4	46	process	process	NOUN
fcis-9856	4	47	to	to	PART
fcis-9856	4	48	simulate	simulate	VERB
fcis-9856	4	49	the	the	DET
fcis-9856	4	50	generation	generation	NOUN
fcis-9856	4	51	process	process	NOUN
fcis-9856	4	52	of	of	ADP
fcis-9856	4	53	low	low	ADJ
fcis-9856	4	54	-	-	PUNCT
fcis-9856	4	55	resolution	resolution	NOUN
fcis-9856	4	56	remote	remote	ADJ
fcis-9856	4	57	sensing	sensing	NOUN
fcis-9856	4	58	images	image	NOUN
fcis-9856	4	59	in	in	ADP
fcis-9856	4	60	natural	natural	ADJ
fcis-9856	4	61	scenes	scene	NOUN
fcis-9856	4	62	,	,	PUNCT
fcis-9856	4	63	and	and	CCONJ
fcis-9856	4	64	generates	generate	VERB
fcis-9856	4	65	realistic	realistic	ADJ
fcis-9856	4	66	low	low	ADJ
fcis-9856	4	67	-	-	PUNCT
fcis-9856	4	68	resolution	resolution	NOUN
fcis-9856	4	69	images	image	NOUN
fcis-9856	4	70	for	for	ADP
fcis-9856	4	71	training	training	NOUN
fcis-9856	4	72	;	;	PUNCT
fcis-9856	4	73	meanwhile	meanwhile	ADV
fcis-9856	4	74	,	,	PUNCT
fcis-9856	4	75	the	the	DET
fcis-9856	4	76	algorithm	algorithm	NOUN
fcis-9856	4	77	uses	use	VERB
fcis-9856	4	78	resnet34	resnet34	NOUN
fcis-9856	4	79	and	and	CCONJ
fcis-9856	4	80	cnn	cnn	PROPN
fcis-9856	4	81	as	as	ADP
fcis-9856	4	82	the	the	DET
fcis-9856	4	83	basis	basis	NOUN
fcis-9856	4	84	to	to	PART
fcis-9856	4	85	enhance	enhance	VERB
fcis-9856	4	86	the	the	DET
fcis-9856	4	87	texture	texture	ADJ
fcis-9856	4	88	details	detail	NOUN
fcis-9856	4	89	of	of	ADP
fcis-9856	4	90	remote	remote	ADJ
fcis-9856	4	91	sensing	sensing	NOUN
fcis-9856	4	92	images	image	NOUN
fcis-9856	4	93	,	,	PUNCT
fcis-9856	4	94	thus	thus	ADV
fcis-9856	4	95	richer	rich	ADJ
fcis-9856	4	96	features	feature	NOUN
fcis-9856	4	97	can	can	AUX
fcis-9856	4	98	be	be	AUX
fcis-9856	4	99	extracted	extract	VERB
fcis-9856	4	100	from	from	ADP
fcis-9856	4	101	the	the	DET
fcis-9856	4	102	images	image	NOUN
fcis-9856	4	103	.	.	PUNCT
fcis-9856	5	1	the	the	DET
fcis-9856	5	2	results	result	NOUN
fcis-9856	5	3	of	of	ADP
fcis-9856	5	4	experiments	experiment	NOUN
fcis-9856	5	5	conducted	conduct	VERB
fcis-9856	5	6	on	on	ADP
fcis-9856	5	7	the	the	DET
fcis-9856	5	8	alsat2	alsat2	PROPN
fcis-9856	5	9	b	b	ADP
fcis-9856	5	10	real	real	ADJ
fcis-9856	5	11	dataset	dataset	NOUN
fcis-9856	5	12	show	show	NOUN
fcis-9856	5	13	that	that	SCONJ
fcis-9856	5	14	the	the	DET
fcis-9856	5	15	method	method	NOUN
fcis-9856	5	16	reduces	reduce	VERB
fcis-9856	5	17	the	the	DET
fcis-9856	5	18	time	time	NOUN
fcis-9856	5	19	and	and	CCONJ
fcis-9856	5	20	cost	cost	NOUN
fcis-9856	5	21	required	require	VERB
fcis-9856	5	22	for	for	ADP
fcis-9856	5	23	sample	sample	NOUN
fcis-9856	5	24	data	datum	NOUN
fcis-9856	5	25	acquisition	acquisition	NOUN
fcis-9856	5	26	,	,	PUNCT
fcis-9856	5	27	optimizes	optimize	VERB
fcis-9856	5	28	the	the	DET
fcis-9856	5	29	processing	processing	NOUN
fcis-9856	5	30	speed	speed	NOUN
fcis-9856	5	31	,	,	PUNCT
fcis-9856	5	32	enhances	enhance	VERB
fcis-9856	5	33	the	the	DET
fcis-9856	5	34	temporal	temporal	ADJ
fcis-9856	5	35	and	and	CCONJ
fcis-9856	5	36	spatial	spatial	ADJ
fcis-9856	5	37	resolution	resolution	NOUN
fcis-9856	5	38	of	of	ADP
fcis-9856	5	39	remote	remote	ADJ
fcis-9856	5	40	sensing	sensing	NOUN
fcis-9856	5	41	images	image	NOUN
fcis-9856	5	42	,	,	PUNCT
fcis-9856	5	43	and	and	CCONJ
fcis-9856	5	44	improves	improve	VERB
fcis-9856	5	45	the	the	DET
fcis-9856	5	46	effect	effect	NOUN
fcis-9856	5	47	of	of	ADP
fcis-9856	5	48	super	super	ADJ
fcis-9856	5	49	-	-	ADJ
fcis-9856	5	50	resolution	resolution	ADJ
fcis-9856	5	51	processing	processing	NOUN
fcis-9856	5	52	.	.	PUNCT
fcis-9856	6	1	keywords	keyword	NOUN
fcis-9856	6	2	:	:	PUNCT
fcis-9856	6	3	data	datum	NOUN
fcis-9856	6	4	enhancement	enhancement	NOUN
fcis-9856	6	5	;	;	PUNCT
fcis-9856	6	6	super	super	NOUN
fcis-9856	6	7	-	-	NOUN
fcis-9856	6	8	resolution	resolution	NOUN
fcis-9856	6	9	;	;	PUNCT
fcis-9856	6	10	remote	remote	ADJ
fcis-9856	6	11	sensing	sensing	NOUN
fcis-9856	6	12	images	image	NOUN
fcis-9856	6	13	;	;	PUNCT
fcis-9856	6	14	generative	generative	ADJ
fcis-9856	6	15	adversarial	adversarial	ADJ
fcis-9856	6	16	networks	network	NOUN
fcis-9856	6	17	.	.	PUNCT
fcis-9856	7	1	1	1	X
fcis-9856	7	2	.	.	X
fcis-9856	7	3	introduction	introduction	NOUN
fcis-9856	7	4	with	with	ADP
fcis-9856	7	5	the	the	DET
fcis-9856	7	6	development	development	NOUN
fcis-9856	7	7	of	of	ADP
fcis-9856	7	8	remote	remote	ADJ
fcis-9856	7	9	sensing	sense	VERB
fcis-9856	7	10	satellite	satellite	NOUN
fcis-9856	7	11	technology	technology	NOUN
fcis-9856	7	12	,	,	PUNCT
fcis-9856	7	13	the	the	DET
fcis-9856	7	14	current	current	ADJ
fcis-9856	7	15	high	high	ADJ
fcis-9856	7	16	-	-	PUNCT
fcis-9856	7	17	resolution	resolution	NOUN
fcis-9856	7	18	satellites	satellite	NOUN
fcis-9856	7	19	of	of	ADP
fcis-9856	7	20	various	various	ADJ
fcis-9856	7	21	countries	country	NOUN
fcis-9856	7	22	have	have	AUX
fcis-9856	7	23	reached	reach	VERB
fcis-9856	7	24	the	the	DET
fcis-9856	7	25	centimeter	centimeter	NOUN
fcis-9856	7	26	level	level	NOUN
fcis-9856	7	27	.	.	PUNCT
fcis-9856	8	1	in	in	ADP
fcis-9856	8	2	the	the	DET
fcis-9856	8	3	field	field	NOUN
fcis-9856	8	4	of	of	ADP
fcis-9856	8	5	remote	remote	ADJ
fcis-9856	8	6	sensing	sense	VERB
fcis-9856	8	7	satellite	satellite	NOUN
fcis-9856	8	8	images	image	NOUN
fcis-9856	8	9	,	,	PUNCT
fcis-9856	8	10	the	the	DET
fcis-9856	8	11	remote	remote	ADJ
fcis-9856	8	12	sensing	sensing	NOUN
fcis-9856	8	13	images	image	NOUN
fcis-9856	8	14	acquired	acquire	VERB
fcis-9856	8	15	by	by	ADP
fcis-9856	8	16	users	user	NOUN
fcis-9856	8	17	are	be	AUX
fcis-9856	8	18	map	map	VERB
fcis-9856	8	19	products	product	NOUN
fcis-9856	8	20	distributed	distribute	VERB
fcis-9856	8	21	by	by	ADP
fcis-9856	8	22	satellite	satellite	NOUN
fcis-9856	8	23	centers	center	NOUN
fcis-9856	8	24	after	after	ADP
fcis-9856	8	25	imaging	imaging	NOUN
fcis-9856	8	26	processing	processing	NOUN
fcis-9856	8	27	,	,	PUNCT
fcis-9856	8	28	so	so	CCONJ
fcis-9856	8	29	the	the	DET
fcis-9856	8	30	resolution	resolution	NOUN
fcis-9856	8	31	of	of	ADP
fcis-9856	8	32	images	image	NOUN
fcis-9856	8	33	can	can	AUX
fcis-9856	8	34	no	no	ADV
fcis-9856	8	35	longer	long	ADV
fcis-9856	8	36	be	be	AUX
fcis-9856	8	37	improved	improve	VERB
fcis-9856	8	38	by	by	ADP
fcis-9856	8	39	adjusting	adjust	VERB
fcis-9856	8	40	the	the	DET
fcis-9856	8	41	hardware	hardware	NOUN
fcis-9856	8	42	.	.	PUNCT
fcis-9856	9	1	if	if	SCONJ
fcis-9856	9	2	the	the	DET
fcis-9856	9	3	resolution	resolution	NOUN
fcis-9856	9	4	of	of	ADP
fcis-9856	9	5	the	the	DET
fcis-9856	9	6	finished	finished	ADJ
fcis-9856	9	7	satellite	satellite	NOUN
fcis-9856	9	8	images	image	NOUN
fcis-9856	9	9	is	be	AUX
fcis-9856	9	10	to	to	PART
fcis-9856	9	11	be	be	AUX
fcis-9856	9	12	improved	improve	VERB
fcis-9856	9	13	,	,	PUNCT
fcis-9856	9	14	the	the	DET
fcis-9856	9	15	images	image	NOUN
fcis-9856	9	16	can	can	AUX
fcis-9856	9	17	only	only	ADV
fcis-9856	9	18	be	be	AUX
fcis-9856	9	19	processed	process	VERB
fcis-9856	9	20	by	by	ADP
fcis-9856	9	21	software	software	NOUN
fcis-9856	9	22	.	.	PUNCT
fcis-9856	10	1	image	image	NOUN
fcis-9856	10	2	super	super	ADJ
fcis-9856	10	3	-	-	ADJ
fcis-9856	10	4	resolution	resolution	ADJ
fcis-9856	10	5	reconstruction	reconstruction	NOUN
fcis-9856	10	6	techniques	technique	NOUN
fcis-9856	10	7	can	can	AUX
fcis-9856	10	8	reconstruct	reconstruct	VERB
fcis-9856	10	9	the	the	DET
fcis-9856	10	10	corresponding	corresponding	ADJ
fcis-9856	10	11	high	high	ADJ
fcis-9856	10	12	-	-	PUNCT
fcis-9856	10	13	resolution	resolution	NOUN
fcis-9856	10	14	images	image	NOUN
fcis-9856	10	15	based	base	VERB
fcis-9856	10	16	on	on	ADP
fcis-9856	10	17	low	low	ADJ
fcis-9856	10	18	-	-	PUNCT
fcis-9856	10	19	resolution	resolution	NOUN
fcis-9856	10	20	images	image	NOUN
fcis-9856	10	21	.	.	PUNCT
fcis-9856	11	1	after	after	ADP
fcis-9856	11	2	years	year	NOUN
fcis-9856	11	3	of	of	ADP
fcis-9856	11	4	development	development	NOUN
fcis-9856	11	5	,	,	PUNCT
fcis-9856	11	6	the	the	DET
fcis-9856	11	7	research	research	NOUN
fcis-9856	11	8	focus	focus	NOUN
fcis-9856	11	9	has	have	AUX
fcis-9856	11	10	shifted	shift	VERB
fcis-9856	11	11	from	from	ADP
fcis-9856	11	12	traditional	traditional	ADJ
fcis-9856	11	13	interpolation	interpolation	NOUN
fcis-9856	11	14	and	and	CCONJ
fcis-9856	11	15	reconstruction	reconstruction	NOUN
fcis-9856	11	16	methods	method	NOUN
fcis-9856	11	17	to	to	ADP
fcis-9856	11	18	methods	method	NOUN
fcis-9856	11	19	based	base	VERB
fcis-9856	11	20	on	on	ADP
fcis-9856	11	21	deep	deep	ADJ
fcis-9856	11	22	learning	learning	NOUN
fcis-9856	11	23	.	.	PUNCT
fcis-9856	12	1	dong	dong	NOUN
fcis-9856	12	2	et	et	PROPN
fcis-9856	12	3	al.[1	al.[1	PROPN
fcis-9856	12	4	]	]	PUNCT
fcis-9856	12	5	first	first	ADV
fcis-9856	12	6	combined	combine	VERB
fcis-9856	12	7	convolutional	convolutional	ADJ
fcis-9856	12	8	neural	neural	ADJ
fcis-9856	12	9	networks	network	NOUN
fcis-9856	12	10	with	with	ADP
fcis-9856	12	11	super	super	ADJ
fcis-9856	12	12	-	-	ADJ
fcis-9856	12	13	resolution	resolution	ADJ
fcis-9856	12	14	image	image	NOUN
fcis-9856	12	15	reconstruction	reconstruction	NOUN
fcis-9856	12	16	techniques	technique	NOUN
fcis-9856	12	17	and	and	CCONJ
fcis-9856	12	18	proposed	propose	VERB
fcis-9856	12	19	the	the	DET
fcis-9856	12	20	srcnn	srcnn	NOUN
fcis-9856	12	21	algorithm	algorithm	NOUN
fcis-9856	12	22	,	,	PUNCT
fcis-9856	12	23	and	and	CCONJ
fcis-9856	12	24	then	then	ADV
fcis-9856	12	25	a	a	DET
fcis-9856	12	26	large	large	ADJ
fcis-9856	12	27	number	number	NOUN
fcis-9856	12	28	of	of	ADP
fcis-9856	12	29	super	super	ADJ
fcis-9856	12	30	-	-	ADJ
fcis-9856	12	31	resolution	resolution	ADJ
fcis-9856	12	32	reconstruction	reconstruction	NOUN
fcis-9856	12	33	methods	method	NOUN
fcis-9856	12	34	based	base	VERB
fcis-9856	12	35	on	on	ADP
fcis-9856	12	36	deep	deep	ADJ
fcis-9856	12	37	learning	learning	NOUN
fcis-9856	12	38	began	begin	VERB
fcis-9856	12	39	to	to	PART
fcis-9856	12	40	appear	appear	VERB
fcis-9856	12	41	.	.	PUNCT
fcis-9856	13	1	kim	kim	PROPN
fcis-9856	13	2	et	et	PROPN
fcis-9856	13	3	al.[2]also	al.[2]also	ADV
fcis-9856	13	4	introduced	introduce	VERB
fcis-9856	13	5	the	the	DET
fcis-9856	13	6	vgg	vgg	ADJ
fcis-9856	13	7	network	network	NOUN
fcis-9856	13	8	structure	structure	NOUN
fcis-9856	13	9	to	to	PART
fcis-9856	13	10	increase	increase	VERB
fcis-9856	13	11	the	the	DET
fcis-9856	13	12	number	number	NOUN
fcis-9856	13	13	of	of	ADP
fcis-9856	13	14	network	network	NOUN
fcis-9856	13	15	layers	layer	NOUN
fcis-9856	13	16	and	and	CCONJ
fcis-9856	13	17	use	use	VERB
fcis-9856	13	18	different	different	ADJ
fcis-9856	13	19	sizes	size	NOUN
fcis-9856	13	20	of	of	ADP
fcis-9856	13	21	perceptual	perceptual	ADJ
fcis-9856	13	22	fields	field	NOUN
fcis-9856	13	23	to	to	PART
fcis-9856	13	24	comprehensively	comprehensively	ADV
fcis-9856	13	25	extract	extract	VERB
fcis-9856	13	26	image	image	NOUN
fcis-9856	13	27	detail	detail	NOUN
fcis-9856	13	28	information	information	NOUN
fcis-9856	13	29	in	in	ADP
fcis-9856	13	30	the	the	DET
fcis-9856	13	31	shallow	shallow	ADJ
fcis-9856	13	32	,	,	PUNCT
fcis-9856	13	33	intermediate	intermediate	ADJ
fcis-9856	13	34	,	,	PUNCT
fcis-9856	13	35	and	and	CCONJ
fcis-9856	13	36	high	high	ADJ
fcis-9856	13	37	layers	layer	NOUN
fcis-9856	13	38	,	,	PUNCT
fcis-9856	13	39	solving	solve	VERB
fcis-9856	13	40	the	the	DET
fcis-9856	13	41	problem	problem	NOUN
fcis-9856	13	42	that	that	PRON
fcis-9856	13	43	the	the	DET
fcis-9856	13	44	srcnn	srcnn	NOUN
fcis-9856	13	45	algorithm	algorithm	PROPN
fcis-9856	13	46	relies	rely	VERB
fcis-9856	13	47	on	on	ADP
fcis-9856	13	48	feature	feature	NOUN
fcis-9856	13	49	information	information	NOUN
fcis-9856	13	50	of	of	ADP
fcis-9856	13	51	small	small	ADJ
fcis-9856	13	52	image	image	NOUN
fcis-9856	13	53	regions	region	NOUN
fcis-9856	13	54	.	.	PUNCT
fcis-9856	14	1	he	he	PRON
fcis-9856	14	2	et	et	PROPN
fcis-9856	14	3	al.[3	al.[3	PROPN
fcis-9856	14	4	]	]	PUNCT
fcis-9856	14	5	stacked	stack	VERB
fcis-9856	14	6	multiple	multiple	ADJ
fcis-9856	14	7	residual	residual	ADJ
fcis-9856	14	8	blocks	block	NOUN
fcis-9856	14	9	to	to	PART
fcis-9856	14	10	form	form	VERB
fcis-9856	14	11	a	a	DET
fcis-9856	14	12	residual	residual	ADJ
fcis-9856	14	13	network	network	NOUN
fcis-9856	14	14	(	(	PUNCT
fcis-9856	14	15	resnet	resnet	NOUN
fcis-9856	14	16	)	)	PUNCT
fcis-9856	14	17	to	to	PART
fcis-9856	14	18	solve	solve	VERB
fcis-9856	14	19	the	the	DET
fcis-9856	14	20	problem	problem	NOUN
fcis-9856	14	21	of	of	ADP
fcis-9856	14	22	network	network	NOUN
fcis-9856	14	23	degradation	degradation	NOUN
fcis-9856	14	24	due	due	ADP
fcis-9856	14	25	to	to	ADP
fcis-9856	14	26	the	the	DET
fcis-9856	14	27	deep	deep	ADJ
fcis-9856	14	28	structure	structure	NOUN
fcis-9856	14	29	of	of	ADP
fcis-9856	14	30	the	the	DET
fcis-9856	14	31	convolutional	convolutional	ADJ
fcis-9856	14	32	network	network	NOUN
fcis-9856	14	33	,	,	PUNCT
fcis-9856	14	34	and	and	CCONJ
fcis-9856	14	35	used	use	VERB
fcis-9856	14	36	the	the	DET
fcis-9856	14	37	jump	jump	NOUN
fcis-9856	14	38	connection	connection	NOUN
fcis-9856	14	39	between	between	ADP
fcis-9856	14	40	residual	residual	ADJ
fcis-9856	14	41	blocks	block	NOUN
fcis-9856	14	42	to	to	PART
fcis-9856	14	43	enhance	enhance	VERB
fcis-9856	14	44	the	the	DET
fcis-9856	14	45	image	image	NOUN
fcis-9856	14	46	feature	feature	NOUN
fcis-9856	14	47	information	information	NOUN
fcis-9856	14	48	transfer	transfer	NOUN
fcis-9856	14	49	on	on	ADP
fcis-9856	14	50	different	different	ADJ
fcis-9856	14	51	layers	layer	NOUN
fcis-9856	14	52	and	and	CCONJ
fcis-9856	14	53	alleviate	alleviate	VERB
fcis-9856	14	54	the	the	DET
fcis-9856	14	55	model	model	NOUN
fcis-9856	14	56	gradient	gradient	NOUN
fcis-9856	14	57	disappearance	disappearance	NOUN
fcis-9856	14	58	problem	problem	NOUN
fcis-9856	14	59	.	.	PUNCT
fcis-9856	15	1	after	after	ADP
fcis-9856	15	2	goodfellow	goodfellow	PROPN
fcis-9856	15	3	et	et	PROPN
fcis-9856	15	4	al	al	PROPN
fcis-9856	15	5	.	.	PUNCT
fcis-9856	16	1	[	[	X
fcis-9856	16	2	4	4	X
fcis-9856	16	3	]	]	PUNCT
fcis-9856	16	4	proposed	propose	VERB
fcis-9856	16	5	gan	gan	PROPN
fcis-9856	16	6	,	,	PUNCT
fcis-9856	16	7	many	many	ADJ
fcis-9856	16	8	super	super	ADJ
fcis-9856	16	9	-	-	ADJ
fcis-9856	16	10	resolutions	resolution	NOUN
fcis-9856	16	11	image	image	NOUN
fcis-9856	16	12	reconstruction	reconstruction	NOUN
fcis-9856	16	13	algorithms	algorithm	NOUN
fcis-9856	16	14	based	base	VERB
fcis-9856	16	15	on	on	ADP
fcis-9856	16	16	gan	gan	PROPN
fcis-9856	16	17	have	have	AUX
fcis-9856	16	18	emerged	emerge	VERB
fcis-9856	16	19	,	,	PUNCT
fcis-9856	16	20	which	which	PRON
fcis-9856	16	21	have	have	AUX
fcis-9856	16	22	shown	show	VERB
fcis-9856	16	23	good	good	ADJ
fcis-9856	16	24	results	result	NOUN
fcis-9856	16	25	in	in	ADP
fcis-9856	16	26	terms	term	NOUN
fcis-9856	16	27	of	of	ADP
fcis-9856	16	28	image	image	NOUN
fcis-9856	16	29	reconstruction	reconstruction	NOUN
fcis-9856	16	30	effect	effect	NOUN
fcis-9856	16	31	,	,	PUNCT
fcis-9856	16	32	network	network	NOUN
fcis-9856	16	33	computation	computation	NOUN
fcis-9856	16	34	,	,	PUNCT
fcis-9856	16	35	and	and	CCONJ
fcis-9856	16	36	computation	computation	NOUN
fcis-9856	16	37	speed	speed	NOUN
fcis-9856	16	38	.	.	PUNCT
fcis-9856	17	1	the	the	DET
fcis-9856	17	2	generator	generator	NOUN
fcis-9856	17	3	of	of	ADP
fcis-9856	17	4	srgan	srgan	PROPN
fcis-9856	17	5	is	be	AUX
fcis-9856	17	6	a	a	DET
fcis-9856	17	7	resnet34	resnet34	NOUN
fcis-9856	17	8	architecture	architecture	NOUN
fcis-9856	17	9	that	that	SCONJ
fcis-9856	17	10	extracts	extract	VERB
fcis-9856	17	11	features	feature	NOUN
fcis-9856	17	12	from	from	ADP
fcis-9856	17	13	lr	lr	NOUN
fcis-9856	17	14	images	image	NOUN
fcis-9856	17	15	.	.	PUNCT
fcis-9856	18	1	for	for	ADP
fcis-9856	18	2	upsampling	upsampling	NOUN
fcis-9856	18	3	,	,	PUNCT
fcis-9856	18	4	a	a	DET
fcis-9856	18	5	subpixel	subpixel	ADJ
fcis-9856	18	6	convolutional	convolutional	ADJ
fcis-9856	18	7	layer	layer	NOUN
fcis-9856	18	8	is	be	AUX
fcis-9856	18	9	used	use	VERB
fcis-9856	18	10	.	.	PUNCT
fcis-9856	19	1	the	the	DET
fcis-9856	19	2	srgan	srgan	NOUN
fcis-9856	19	3	model	model	NOUN
fcis-9856	19	4	was	be	AUX
fcis-9856	19	5	proposed	propose	VERB
fcis-9856	19	6	by	by	ADP
fcis-9856	19	7	ledig	ledig	NOUN
fcis-9856	19	8	et	et	NOUN
fcis-9856	19	9	al	al	PROPN
fcis-9856	19	10	.	.	PUNCT
fcis-9856	20	1	[	[	X
fcis-9856	20	2	5	5	X
fcis-9856	20	3	]	]	PUNCT
fcis-9856	20	4	using	use	VERB
fcis-9856	20	5	resnet	resnet	NOUN
fcis-9856	20	6	and	and	CCONJ
fcis-9856	20	7	vgg	vgg	VERB
fcis-9856	20	8	as	as	ADP
fcis-9856	20	9	the	the	DET
fcis-9856	20	10	generator	generator	NOUN
fcis-9856	20	11	and	and	CCONJ
fcis-9856	20	12	discriminator	discriminator	NOUN
fcis-9856	20	13	architectures	architecture	NOUN
fcis-9856	20	14	,	,	PUNCT
fcis-9856	20	15	respectively	respectively	ADV
fcis-9856	20	16	.	.	PUNCT
fcis-9856	21	1	it	it	PRON
fcis-9856	21	2	uses	use	VERB
fcis-9856	21	3	the	the	DET
fcis-9856	21	4	generator	generator	NOUN
fcis-9856	21	5	to	to	PART
fcis-9856	21	6	generate	generate	VERB
fcis-9856	21	7	hr	hr	NOUN
fcis-9856	21	8	images	image	NOUN
fcis-9856	21	9	,	,	PUNCT
fcis-9856	21	10	the	the	DET
fcis-9856	21	11	discriminator	discriminator	NOUN
fcis-9856	21	12	to	to	PART
fcis-9856	21	13	discriminate	discriminate	VERB
fcis-9856	21	14	between	between	ADP
fcis-9856	21	15	the	the	DET
fcis-9856	21	16	reconstructed	reconstructed	ADJ
fcis-9856	21	17	hr	hr	NOUN
fcis-9856	21	18	images	image	NOUN
fcis-9856	21	19	and	and	CCONJ
fcis-9856	21	20	the	the	DET
fcis-9856	21	21	original	original	ADJ
fcis-9856	21	22	hr	hr	NOUN
fcis-9856	21	23	images	image	NOUN
fcis-9856	21	24	,	,	PUNCT
fcis-9856	21	25	and	and	CCONJ
fcis-9856	21	26	the	the	DET
fcis-9856	21	27	backward	backward	ADJ
fcis-9856	21	28	optimization	optimization	NOUN
fcis-9856	21	29	of	of	ADP
fcis-9856	21	30	the	the	DET
fcis-9856	21	31	generator	generator	NOUN
fcis-9856	21	32	and	and	CCONJ
fcis-9856	21	33	discriminator	discriminator	NOUN
fcis-9856	21	34	networks	network	NOUN
fcis-9856	21	35	,	,	PUNCT
fcis-9856	21	36	while	while	SCONJ
fcis-9856	21	37	the	the	DET
fcis-9856	21	38	traditional	traditional	ADJ
fcis-9856	21	39	mse	mse	NOUN
fcis-9856	21	40	loss	loss	NOUN
fcis-9856	21	41	function	function	NOUN
fcis-9856	21	42	is	be	AUX
fcis-9856	21	43	replaced	replace	VERB
fcis-9856	21	44	by	by	ADP
fcis-9856	21	45	the	the	DET
fcis-9856	21	46	"	"	PUNCT
fcis-9856	21	47	perceptual	perceptual	ADJ
fcis-9856	21	48	loss	loss	NOUN
fcis-9856	21	49	"	"	PUNCT
fcis-9856	21	50	to	to	PART
fcis-9856	21	51	enhance	enhance	VERB
fcis-9856	21	52	the	the	DET
fcis-9856	21	53	image	image	NOUN
fcis-9856	21	54	detail	detail	NOUN
fcis-9856	21	55	information	information	NOUN
fcis-9856	21	56	recovery	recovery	NOUN
fcis-9856	21	57	.	.	PUNCT
fcis-9856	22	1	vu	vu	X
fcis-9856	22	2	et	et	PROPN
fcis-9856	22	3	al	al	PROPN
fcis-9856	22	4	.	.	PUNCT
fcis-9856	23	1	[	[	X
fcis-9856	23	2	6	6	NUM
fcis-9856	23	3	]	]	PUNCT
fcis-9856	23	4	used	use	VERB
fcis-9856	23	5	relative	relative	ADJ
fcis-9856	23	6	generative	generative	ADJ
fcis-9856	23	7	adversarial	adversarial	ADJ
fcis-9856	23	8	network	network	NOUN
fcis-9856	23	9	to	to	PART
fcis-9856	23	10	replace	replace	VERB
fcis-9856	23	11	the	the	DET
fcis-9856	23	12	generative	generative	ADJ
fcis-9856	23	13	adversarial	adversarial	ADJ
fcis-9856	23	14	network	network	NOUN
fcis-9856	23	15	in	in	ADP
fcis-9856	23	16	srgan	srgan	NOUN
fcis-9856	23	17	,	,	PUNCT
fcis-9856	23	18	which	which	PRON
fcis-9856	23	19	makes	make	VERB
fcis-9856	23	20	the	the	DET
fcis-9856	23	21	extraction	extraction	NOUN
fcis-9856	23	22	and	and	CCONJ
fcis-9856	23	23	fusion	fusion	NOUN
fcis-9856	23	24	of	of	ADP
fcis-9856	23	25	image	image	NOUN
fcis-9856	23	26	details	detail	NOUN
fcis-9856	23	27	more	more	ADV
fcis-9856	23	28	reasonable	reasonable	ADJ
fcis-9856	23	29	and	and	CCONJ
fcis-9856	23	30	reduces	reduce	VERB
fcis-9856	23	31	the	the	DET
fcis-9856	23	32	influence	influence	NOUN
fcis-9856	23	33	of	of	ADP
fcis-9856	23	34	noise	noise	NOUN
fcis-9856	23	35	and	and	CCONJ
fcis-9856	23	36	blur	blur	PROPN
fcis-9856	23	37	.	.	PROPN
fcis-9856	23	38	compared	compare	VERB
fcis-9856	23	39	with	with	ADP
fcis-9856	23	40	natural	natural	ADJ
fcis-9856	23	41	images	image	NOUN
fcis-9856	23	42	,	,	PUNCT
fcis-9856	23	43	remotely	remotely	ADV
fcis-9856	23	44	sensed	sense	VERB
fcis-9856	23	45	images	image	NOUN
fcis-9856	23	46	contain	contain	VERB
fcis-9856	23	47	rich	rich	ADJ
fcis-9856	23	48	topographical	topographical	ADJ
fcis-9856	23	49	features	feature	NOUN
fcis-9856	23	50	such	such	ADJ
fcis-9856	23	51	as	as	ADP
fcis-9856	23	52	canyons	canyon	NOUN
fcis-9856	23	53	,	,	PUNCT
fcis-9856	23	54	mountains	mountain	NOUN
fcis-9856	23	55	,	,	PUNCT
fcis-9856	23	56	and	and	CCONJ
fcis-9856	23	57	cities	city	NOUN
fcis-9856	23	58	within	within	ADP
fcis-9856	23	59	a	a	DET
fcis-9856	23	60	smaller	small	ADJ
fcis-9856	23	61	pixel	pixel	NOUN
fcis-9856	23	62	size	size	NOUN
fcis-9856	23	63	,	,	PUNCT
fcis-9856	23	64	and	and	CCONJ
fcis-9856	23	65	thus	thus	ADV
fcis-9856	23	66	detail	detail	NOUN
fcis-9856	23	67	loss	loss	NOUN
fcis-9856	23	68	is	be	AUX
fcis-9856	23	69	more	more	ADV
fcis-9856	23	70	serious	serious	ADJ
fcis-9856	23	71	.	.	PUNCT
fcis-9856	24	1	in	in	ADP
fcis-9856	24	2	addition	addition	NOUN
fcis-9856	24	3	,	,	PUNCT
fcis-9856	24	4	remote	remote	ADJ
fcis-9856	24	5	sensing	sensing	NOUN
fcis-9856	24	6	images	image	NOUN
fcis-9856	24	7	are	be	AUX
fcis-9856	24	8	also	also	ADV
fcis-9856	24	9	affected	affect	VERB
fcis-9856	24	10	by	by	ADP
fcis-9856	24	11	atmospheric	atmospheric	ADJ
fcis-9856	24	12	disturbance	disturbance	NOUN
fcis-9856	24	13	,	,	PUNCT
fcis-9856	24	14	cloud	cloud	NOUN
fcis-9856	24	15	obscuration	obscuration	NOUN
fcis-9856	24	16	,	,	PUNCT
fcis-9856	24	17	noise	noise	NOUN
fcis-9856	24	18	and	and	CCONJ
fcis-9856	24	19	motion	motion	NOUN
fcis-9856	24	20	blur	blur	NOUN
fcis-9856	24	21	during	during	ADP
fcis-9856	24	22	the	the	DET
fcis-9856	24	23	acquisition	acquisition	NOUN
fcis-9856	24	24	process	process	NOUN
fcis-9856	24	25	,	,	PUNCT
fcis-9856	24	26	which	which	PRON
fcis-9856	24	27	makes	make	VERB
fcis-9856	24	28	super	super	ADJ
fcis-9856	24	29	-	-	ADJ
fcis-9856	24	30	resolution	resolution	ADJ
fcis-9856	24	31	reconstruction	reconstruction	NOUN
fcis-9856	24	32	more	more	ADV
fcis-9856	24	33	difficult	difficult	ADJ
fcis-9856	24	34	.	.	PUNCT
fcis-9856	25	1	due	due	ADP
fcis-9856	25	2	to	to	ADP
fcis-9856	25	3	the	the	DET
fcis-9856	25	4	difficulty	difficulty	NOUN
fcis-9856	25	5	of	of	ADP
fcis-9856	25	6	remote	remote	ADJ
fcis-9856	25	7	sensing	sense	VERB
fcis-9856	25	8	data	datum	NOUN
fcis-9856	25	9	set	set	VERB
fcis-9856	25	10	collection	collection	NOUN
fcis-9856	25	11	and	and	CCONJ
fcis-9856	25	12	the	the	DET
fcis-9856	25	13	lack	lack	NOUN
fcis-9856	25	14	of	of	ADP
fcis-9856	25	15	corresponding	correspond	VERB
fcis-9856	25	16	high	high	ADJ
fcis-9856	25	17	-	-	PUNCT
fcis-9856	25	18	resolution	resolution	NOUN
fcis-9856	25	19	and	and	CCONJ
fcis-9856	25	20	low	low	ADJ
fcis-9856	25	21	-	-	PUNCT
fcis-9856	25	22	resolution	resolution	NOUN
fcis-9856	25	23	remote	remote	ADJ
fcis-9856	25	24	sensing	sense	VERB
fcis-9856	25	25	image	image	NOUN
fcis-9856	25	26	pairs	pair	NOUN
fcis-9856	25	27	for	for	ADP
fcis-9856	25	28	algorithm	algorithm	NOUN
fcis-9856	25	29	training	training	NOUN
fcis-9856	25	30	,	,	PUNCT
fcis-9856	25	31	the	the	DET
fcis-9856	25	32	effect	effect	NOUN
fcis-9856	25	33	of	of	ADP
fcis-9856	25	34	downstream	downstream	ADJ
fcis-9856	25	35	tasks	task	NOUN
fcis-9856	25	36	such	such	ADJ
fcis-9856	25	37	as	as	ADP
fcis-9856	25	38	remote	remote	ADJ
fcis-9856	25	39	sensing	sense	VERB
fcis-9856	25	40	image	image	NOUN
fcis-9856	25	41	classification	classification	NOUN
fcis-9856	25	42	and	and	CCONJ
fcis-9856	25	43	target	target	NOUN
fcis-9856	25	44	recognition	recognition	NOUN
fcis-9856	25	45	can	can	AUX
fcis-9856	25	46	be	be	AUX
fcis-9856	25	47	effectively	effectively	ADV
fcis-9856	25	48	improved	improve	VERB
fcis-9856	25	49	after	after	ADP
fcis-9856	25	50	super	super	ADJ
fcis-9856	25	51	-	-	ADJ
fcis-9856	25	52	resolution	resolution	ADJ
fcis-9856	25	53	processing	processing	NOUN
fcis-9856	25	54	of	of	ADP
fcis-9856	25	55	remote	remote	ADJ
fcis-9856	25	56	sensing	sensing	NOUN
fcis-9856	25	57	images	image	NOUN
fcis-9856	25	58	.	.	PUNCT
fcis-9856	26	1	aiming	aim	VERB
fcis-9856	26	2	at	at	ADP
fcis-9856	26	3	the	the	DET
fcis-9856	26	4	problems	problem	NOUN
fcis-9856	26	5	of	of	ADP
fcis-9856	26	6	insufficient	insufficient	ADJ
fcis-9856	26	7	sample	sample	NOUN
fcis-9856	26	8	data	datum	NOUN
fcis-9856	26	9	of	of	ADP
fcis-9856	26	10	remote	remote	ADJ
fcis-9856	26	11	sensing	sensing	NOUN
fcis-9856	26	12	images	image	NOUN
fcis-9856	26	13	and	and	CCONJ
fcis-9856	26	14	poor	poor	ADJ
fcis-9856	26	15	recovery	recovery	NOUN
fcis-9856	26	16	effect	effect	NOUN
fcis-9856	26	17	of	of	ADP
fcis-9856	26	18	super	super	ADJ
fcis-9856	26	19	-	-	ADJ
fcis-9856	26	20	resolution	resolution	ADJ
fcis-9856	26	21	reconstruction	reconstruction	NOUN
fcis-9856	26	22	,	,	PUNCT
fcis-9856	26	23	this	this	DET
fcis-9856	26	24	paper	paper	NOUN
fcis-9856	26	25	proposes	propose	VERB
fcis-9856	26	26	a	a	DET
fcis-9856	26	27	super	super	ADJ
fcis-9856	26	28	-	-	ADJ
fcis-9856	26	29	resolution	resolution	ADJ
fcis-9856	26	30	reconstruction	reconstruction	NOUN
fcis-9856	26	31	algorithm	algorithm	NOUN
fcis-9856	26	32	for	for	ADP
fcis-9856	26	33	remote	remote	ADJ
fcis-9856	26	34	sensing	sensing	NOUN
fcis-9856	26	35	images	image	NOUN
fcis-9856	26	36	based	base	VERB
fcis-9856	26	37	on	on	ADP
fcis-9856	26	38	data	data	NOUN
fcis-9856	26	39	enhancement	enhancement	NOUN
fcis-9856	26	40	and	and	CCONJ
fcis-9856	26	41	generative	generative	ADJ
fcis-9856	26	42	adversarial	adversarial	ADJ
fcis-9856	26	43	network	network	NOUN
fcis-9856	26	44	.	.	PUNCT
fcis-9856	27	1	data	datum	NOUN
fcis-9856	27	2	augmentation	augmentation	NOUN
fcis-9856	27	3	expands	expand	VERB
fcis-9856	27	4	the	the	DET
fcis-9856	27	5	training	training	NOUN
fcis-9856	27	6	set	set	VERB
fcis-9856	27	7	by	by	ADP
fcis-9856	27	8	systematically	systematically	ADV
fcis-9856	27	9	generating	generate	VERB
fcis-9856	27	10	more	more	ADJ
fcis-9856	27	11	training	training	NOUN
fcis-9856	27	12	samples	sample	NOUN
fcis-9856	27	13	,	,	PUNCT
fcis-9856	27	14	32	32	NUM
fcis-9856	27	15	increases	increase	VERB
fcis-9856	27	16	the	the	DET
fcis-9856	27	17	amount	amount	NOUN
fcis-9856	27	18	of	of	ADP
fcis-9856	27	19	data	datum	NOUN
fcis-9856	27	20	for	for	ADP
fcis-9856	27	21	training	training	NOUN
fcis-9856	27	22	,	,	PUNCT
fcis-9856	27	23	and	and	CCONJ
fcis-9856	27	24	improves	improve	VERB
fcis-9856	27	25	the	the	DET
fcis-9856	27	26	generalization	generalization	NOUN
fcis-9856	27	27	ability	ability	NOUN
fcis-9856	27	28	and	and	CCONJ
fcis-9856	27	29	robustness	robustness	NOUN
fcis-9856	27	30	of	of	ADP
fcis-9856	27	31	the	the	DET
fcis-9856	27	32	model	model	NOUN
fcis-9856	27	33	,	,	PUNCT
fcis-9856	27	34	which	which	PRON
fcis-9856	27	35	has	have	AUX
fcis-9856	27	36	achieved	achieve	VERB
fcis-9856	27	37	good	good	ADJ
fcis-9856	27	38	results	result	NOUN
fcis-9856	27	39	in	in	ADP
fcis-9856	27	40	the	the	DET
fcis-9856	27	41	fields	field	NOUN
fcis-9856	27	42	of	of	ADP
fcis-9856	27	43	machine	machine	NOUN
fcis-9856	27	44	learning	learning	NOUN
fcis-9856	27	45	,	,	PUNCT
fcis-9856	27	46	image	image	NOUN
fcis-9856	27	47	recognition	recognition	NOUN
fcis-9856	27	48	and	and	CCONJ
fcis-9856	27	49	image	image	NOUN
fcis-9856	27	50	restoration	restoration	NOUN
fcis-9856	27	51	;	;	PUNCT
fcis-9856	27	52	generative	generative	ADJ
fcis-9856	27	53	adversarial	adversarial	ADJ
fcis-9856	27	54	networks	network	NOUN
fcis-9856	27	55	have	have	AUX
fcis-9856	27	56	also	also	ADV
fcis-9856	27	57	proved	prove	VERB
fcis-9856	27	58	to	to	PART
fcis-9856	27	59	be	be	AUX
fcis-9856	27	60	effective	effective	ADJ
fcis-9856	27	61	in	in	ADP
fcis-9856	27	62	the	the	DET
fcis-9856	27	63	field	field	NOUN
fcis-9856	27	64	of	of	ADP
fcis-9856	27	65	image	image	NOUN
fcis-9856	27	66	processing	processing	NOUN
fcis-9856	27	67	and	and	CCONJ
fcis-9856	27	68	are	be	AUX
fcis-9856	27	69	widely	widely	ADV
fcis-9856	27	70	used	use	VERB
fcis-9856	27	71	.	.	PUNCT
fcis-9856	28	1	the	the	DET
fcis-9856	28	2	algorithm	algorithm	NOUN
fcis-9856	28	3	in	in	ADP
fcis-9856	28	4	this	this	DET
fcis-9856	28	5	paper	paper	NOUN
fcis-9856	28	6	consists	consist	VERB
fcis-9856	28	7	of	of	ADP
fcis-9856	28	8	a	a	DET
fcis-9856	28	9	degradation	degradation	NOUN
fcis-9856	28	10	model	model	NOUN
fcis-9856	28	11	and	and	CCONJ
fcis-9856	28	12	a	a	DET
fcis-9856	28	13	super	super	ADJ
fcis-9856	28	14	-	-	ADJ
fcis-9856	28	15	resolution	resolution	ADJ
fcis-9856	28	16	reconstruction	reconstruction	NOUN
fcis-9856	28	17	model	model	NOUN
fcis-9856	28	18	.	.	PUNCT
fcis-9856	29	1	the	the	DET
fcis-9856	29	2	degradation	degradation	NOUN
fcis-9856	29	3	model	model	NOUN
fcis-9856	29	4	uses	uses	AUX
fcis-9856	29	5	randomly	randomly	ADV
fcis-9856	29	6	mixing	mix	VERB
fcis-9856	29	7	and	and	CCONJ
fcis-9856	29	8	washing	washing	NOUN
fcis-9856	29	9	degradation	degradation	NOUN
fcis-9856	29	10	factors	factor	NOUN
fcis-9856	29	11	(	(	PUNCT
fcis-9856	29	12	such	such	ADJ
fcis-9856	29	13	as	as	ADP
fcis-9856	29	14	blur	blur	NOUN
fcis-9856	29	15	,	,	PUNCT
fcis-9856	29	16	downsampling	downsampling	NOUN
fcis-9856	29	17	,	,	PUNCT
fcis-9856	29	18	noise	noise	NOUN
fcis-9856	29	19	,	,	PUNCT
fcis-9856	29	20	etc	etc	X
fcis-9856	29	21	.	.	X
fcis-9856	29	22	)	)	PUNCT
fcis-9856	29	23	to	to	PART
fcis-9856	29	24	simulate	simulate	VERB
fcis-9856	29	25	the	the	DET
fcis-9856	29	26	real	real	ADJ
fcis-9856	29	27	imaging	imaging	NOUN
fcis-9856	29	28	process	process	NOUN
fcis-9856	29	29	to	to	PART
fcis-9856	29	30	generate	generate	VERB
fcis-9856	29	31	realistic	realistic	ADJ
fcis-9856	29	32	low	low	ADJ
fcis-9856	29	33	-	-	PUNCT
fcis-9856	29	34	resolution	resolution	NOUN
fcis-9856	29	35	images	image	NOUN
fcis-9856	29	36	combined	combine	VERB
fcis-9856	29	37	with	with	ADP
fcis-9856	29	38	the	the	DET
fcis-9856	29	39	original	original	ADJ
fcis-9856	29	40	images	image	NOUN
fcis-9856	29	41	in	in	ADP
fcis-9856	29	42	pairs	pair	NOUN
fcis-9856	29	43	for	for	ADP
fcis-9856	29	44	training	training	NOUN
fcis-9856	29	45	;	;	PUNCT
fcis-9856	29	46	meanwhile	meanwhile	ADV
fcis-9856	29	47	,	,	PUNCT
fcis-9856	29	48	a	a	DET
fcis-9856	29	49	new	new	ADJ
fcis-9856	29	50	image	image	NOUN
fcis-9856	29	51	processing	processing	NOUN
fcis-9856	29	52	architecture	architecture	NOUN
fcis-9856	29	53	based	base	VERB
fcis-9856	29	54	on	on	ADP
fcis-9856	29	55	generative	generative	ADJ
fcis-9856	29	56	adversarial	adversarial	ADJ
fcis-9856	29	57	networks	network	NOUN
fcis-9856	29	58	is	be	AUX
fcis-9856	29	59	proposed	propose	VERB
fcis-9856	29	60	to	to	PART
fcis-9856	29	61	enhance	enhance	VERB
fcis-9856	29	62	image	image	NOUN
fcis-9856	29	63	details	detail	NOUN
fcis-9856	29	64	and	and	CCONJ
fcis-9856	29	65	preserve	preserve	VERB
fcis-9856	29	66	image	image	NOUN
fcis-9856	29	67	features	feature	NOUN
fcis-9856	29	68	through	through	ADP
fcis-9856	29	69	resnet34	resnet34	NOUN
fcis-9856	29	70	and	and	CCONJ
fcis-9856	29	71	mini	mini	NOUN
fcis-9856	29	72	-	-	PROPN
fcis-9856	29	73	cnn	cnn	PROPN
fcis-9856	29	74	networks	network	NOUN
fcis-9856	29	75	,	,	PUNCT
fcis-9856	29	76	and	and	CCONJ
fcis-9856	29	77	the	the	DET
fcis-9856	29	78	method	method	NOUN
fcis-9856	29	79	can	can	AUX
fcis-9856	29	80	better	well	ADV
fcis-9856	29	81	enhance	enhance	VERB
fcis-9856	29	82	the	the	DET
fcis-9856	29	83	effect	effect	NOUN
fcis-9856	29	84	of	of	ADP
fcis-9856	29	85	super	super	ADJ
fcis-9856	29	86	-	-	ADJ
fcis-9856	29	87	resolution	resolution	ADJ
fcis-9856	29	88	reconstruction	reconstruction	NOUN
fcis-9856	29	89	of	of	ADP
fcis-9856	29	90	remote	remote	ADJ
fcis-9856	29	91	sensing	sensing	NOUN
fcis-9856	29	92	images	image	NOUN
fcis-9856	29	93	.	.	PUNCT
fcis-9856	30	1	2	2	X
fcis-9856	30	2	.	.	X
fcis-9856	30	3	methodology	methodology	NOUN
fcis-9856	30	4	of	of	ADP
fcis-9856	30	5	this	this	DET
fcis-9856	30	6	paper	paper	NOUN
fcis-9856	30	7	for	for	ADP
fcis-9856	30	8	the	the	DET
fcis-9856	30	9	super	super	ADJ
fcis-9856	30	10	-	-	ADJ
fcis-9856	30	11	resolution	resolution	ADJ
fcis-9856	30	12	reconstruction	reconstruction	NOUN
fcis-9856	30	13	of	of	ADP
fcis-9856	30	14	remote	remote	ADJ
fcis-9856	30	15	sensing	sensing	NOUN
fcis-9856	30	16	images	image	NOUN
fcis-9856	30	17	that	that	PRON
fcis-9856	30	18	lack	lack	VERB
fcis-9856	30	19	paired	pair	VERB
fcis-9856	30	20	datasets	dataset	NOUN
fcis-9856	30	21	for	for	ADP
fcis-9856	30	22	training	training	NOUN
fcis-9856	31	1	,	,	PUNCT
fcis-9856	31	2	this	this	DET
fcis-9856	31	3	paper	paper	NOUN
fcis-9856	31	4	proposes	propose	VERB
fcis-9856	31	5	a	a	DET
fcis-9856	31	6	method	method	NOUN
fcis-9856	31	7	consisting	consist	VERB
fcis-9856	31	8	of	of	ADP
fcis-9856	31	9	a	a	DET
fcis-9856	31	10	degradation	degradation	NOUN
fcis-9856	31	11	model	model	NOUN
fcis-9856	31	12	and	and	CCONJ
fcis-9856	31	13	a	a	DET
fcis-9856	31	14	reconstruction	reconstruction	NOUN
fcis-9856	31	15	model	model	NOUN
fcis-9856	31	16	.	.	PUNCT
fcis-9856	32	1	the	the	DET
fcis-9856	32	2	overall	overall	ADJ
fcis-9856	32	3	network	network	NOUN
fcis-9856	32	4	framework	framework	NOUN
fcis-9856	32	5	is	be	AUX
fcis-9856	32	6	shown	show	VERB
fcis-9856	32	7	in	in	ADP
fcis-9856	32	8	figure	figure	NOUN
fcis-9856	32	9	1	1	NUM
fcis-9856	32	10	,	,	PUNCT
fcis-9856	32	11	which	which	PRON
fcis-9856	32	12	is	be	AUX
fcis-9856	32	13	divided	divide	VERB
fcis-9856	32	14	into	into	ADP
fcis-9856	32	15	two	two	NUM
fcis-9856	32	16	stages	stage	NOUN
fcis-9856	32	17	:	:	PUNCT
fcis-9856	32	18	training	training	NOUN
fcis-9856	32	19	and	and	CCONJ
fcis-9856	32	20	testing	testing	NOUN
fcis-9856	32	21	.	.	PUNCT
fcis-9856	33	1	ihr	ihr	AUX
fcis-9856	33	2	discriminating	discriminate	VERB
fcis-9856	33	3	networks	network	NOUN
fcis-9856	33	4	degradation	degradation	NOUN
fcis-9856	33	5	model	model	NOUN
fcis-9856	33	6	super	super	ADJ
fcis-9856	33	7	-	-	ADJ
fcis-9856	33	8	resolution	resolution	ADJ
fcis-9856	33	9	reconstruction	reconstruction	NOUN
fcis-9856	33	10	ilr	ilr	PROPN
fcis-9856	33	11	superresolution	superresolution	NOUN
fcis-9856	33	12	reconstruction	reconstruction	NOUN
fcis-9856	33	13	ihr	ihr	PROPN
fcis-9856	33	14	ihr	ihr	PROPN
fcis-9856	33	15	ihr	ihr	PROPN
fcis-9856	33	16	^	^	PUNCT
fcis-9856	33	17	^	^	PUNCT
fcis-9856	33	18	psnr	psnr	NOUN
fcis-9856	33	19	ssim	ssim	NOUN
fcis-9856	33	20	figure	figure	NOUN
fcis-9856	33	21	1	1	NUM
fcis-9856	33	22	.	.	PUNCT
fcis-9856	34	1	overall	overall	ADJ
fcis-9856	34	2	network	network	NOUN
fcis-9856	34	3	framework	framework	NOUN
fcis-9856	34	4	in	in	ADP
fcis-9856	34	5	the	the	DET
fcis-9856	34	6	training	training	NOUN
fcis-9856	34	7	phase	phase	NOUN
fcis-9856	34	8	,	,	PUNCT
fcis-9856	34	9	the	the	DET
fcis-9856	34	10	degradation	degradation	NOUN
fcis-9856	34	11	model	model	NOUN
fcis-9856	34	12	is	be	AUX
fcis-9856	34	13	used	use	VERB
fcis-9856	34	14	to	to	PART
fcis-9856	34	15	process	process	VERB
fcis-9856	34	16	the	the	DET
fcis-9856	34	17	high	high	ADJ
fcis-9856	34	18	-	-	PUNCT
fcis-9856	34	19	resolution	resolution	NOUN
fcis-9856	34	20	image	image	NOUN
fcis-9856	34	21	ihr	ihr	NOUN
fcis-9856	34	22	in	in	ADP
fcis-9856	34	23	the	the	DET
fcis-9856	34	24	uc	uc	PROPN
fcis-9856	34	25	merced	merced	PROPN
fcis-9856	34	26	dataset	dataset	VERB
fcis-9856	34	27	to	to	PART
fcis-9856	34	28	generate	generate	VERB
fcis-9856	34	29	synthetic	synthetic	ADJ
fcis-9856	34	30	images	image	NOUN
fcis-9856	34	31	with	with	ADP
fcis-9856	34	32	feature	feature	NOUN
fcis-9856	34	33	distributions	distribution	NOUN
fcis-9856	34	34	approximating	approximate	VERB
fcis-9856	34	35	the	the	DET
fcis-9856	34	36	real	real	ADJ
fcis-9856	34	37	low	low	ADJ
fcis-9856	34	38	-	-	PUNCT
fcis-9856	34	39	resolution	resolution	NOUN
fcis-9856	34	40	image	image	NOUN
fcis-9856	34	41	ilr	ilr	NOUN
fcis-9856	34	42	,	,	PUNCT
fcis-9856	34	43	and	and	CCONJ
fcis-9856	34	44	the	the	DET
fcis-9856	34	45	composed	compose	VERB
fcis-9856	34	46	images	image	NOUN
fcis-9856	34	47	are	be	AUX
fcis-9856	34	48	trained	train	VERB
fcis-9856	34	49	on	on	ADP
fcis-9856	34	50	lrî	lrî	PROPN
fcis-9856	34	51	～	～	NOUN
fcis-9856	34	52	ihr	ihr	NOUN
fcis-9856	34	53	,	,	PUNCT
fcis-9856	34	54	and	and	CCONJ
fcis-9856	34	55	the	the	DET
fcis-9856	34	56	ilr	ilr	NOUN
fcis-9856	34	57	is	be	AUX
fcis-9856	34	58	super	super	ADJ
fcis-9856	34	59	-	-	ADJ
fcis-9856	34	60	resolution	resolution	NOUN
fcis-9856	34	61	reconstructed	reconstruct	VERB
fcis-9856	34	62	to	to	PART
fcis-9856	34	63	generate	generate	VERB
fcis-9856	34	64	high	high	ADJ
fcis-9856	34	65	-	-	PUNCT
fcis-9856	34	66	resolution	resolution	NOUN
fcis-9856	34	67	images	image	NOUN
fcis-9856	34	68	hrî	hrî	PROPN
fcis-9856	34	69	,	,	PUNCT
fcis-9856	34	70	and	and	CCONJ
fcis-9856	34	71	the	the	DET
fcis-9856	34	72	discriminative	discriminative	NOUN
fcis-9856	34	73	network	network	NOUN
fcis-9856	34	74	optimizes	optimize	VERB
fcis-9856	34	75	the	the	DET
fcis-9856	34	76	training	training	NOUN
fcis-9856	34	77	process	process	NOUN
fcis-9856	34	78	by	by	ADP
fcis-9856	34	79	reducing	reduce	VERB
fcis-9856	34	80	the	the	DET
fcis-9856	34	81	loss	loss	NOUN
fcis-9856	34	82	between	between	ADP
fcis-9856	34	83	hrî	hrî	PROPN
fcis-9856	34	84	～	～	NOUN
fcis-9856	34	85	ihr	ihr	NOUN
fcis-9856	34	86	;	;	PUNCT
fcis-9856	34	87	in	in	ADP
fcis-9856	34	88	the	the	DET
fcis-9856	34	89	testing	testing	NOUN
fcis-9856	34	90	phase	phase	NOUN
fcis-9856	34	91	,	,	PUNCT
fcis-9856	34	92	the	the	DET
fcis-9856	34	93	al	al	PROPN
fcis-9856	34	94	-	-	PUNCT
fcis-9856	34	95	sat2b	sat2b	PROPN
fcis-9856	34	96	dataset	dataset	NOUN
fcis-9856	34	97	is	be	AUX
fcis-9856	34	98	input	input	NOUN
fcis-9856	34	99	to	to	ADP
fcis-9856	34	100	the	the	DET
fcis-9856	34	101	trained	train	VERB
fcis-9856	34	102	super	super	ADJ
fcis-9856	34	103	-	-	ADJ
fcis-9856	34	104	resolution	resolution	ADJ
fcis-9856	34	105	reconstruction	reconstruction	NOUN
fcis-9856	34	106	model	model	NOUN
fcis-9856	34	107	to	to	PART
fcis-9856	34	108	obtain	obtain	VERB
fcis-9856	34	109	hrî	hrî	PROPN
fcis-9856	34	110	,	,	PUNCT
fcis-9856	34	111	and	and	CCONJ
fcis-9856	34	112	then	then	ADV
fcis-9856	34	113	compare	compare	VERB
fcis-9856	34	114	the	the	DET
fcis-9856	34	115	subjective	subjective	ADJ
fcis-9856	34	116	visual	visual	ADJ
fcis-9856	34	117	effect	effect	NOUN
fcis-9856	34	118	with	with	ADP
fcis-9856	34	119	the	the	DET
fcis-9856	34	120	real	real	ADJ
fcis-9856	34	121	ihr	ihr	NOUN
fcis-9856	34	122	and	and	CCONJ
fcis-9856	34	123	calculate	calculate	VERB
fcis-9856	34	124	psnr	psnr	NOUN
fcis-9856	34	125	/	/	SYM
fcis-9856	34	126	ssim	ssim	NOUN
fcis-9856	34	127	to	to	ADP
fcis-9856	34	128	qualitatively	qualitatively	ADV
fcis-9856	34	129	and	and	CCONJ
fcis-9856	34	130	quantitatively	quantitatively	ADV
fcis-9856	34	131	evaluate	evaluate	VERB
fcis-9856	34	132	the	the	DET
fcis-9856	34	133	reconstruction	reconstruction	NOUN
fcis-9856	34	134	effect	effect	NOUN
fcis-9856	34	135	.	.	PUNCT
fcis-9856	35	1	2.1	2.1	NUM
fcis-9856	35	2	.	.	PUNCT
fcis-9856	35	3	degradation	degradation	NOUN
fcis-9856	35	4	model	model	NOUN
fcis-9856	35	5	in	in	ADP
fcis-9856	35	6	the	the	DET
fcis-9856	35	7	super	super	ADJ
fcis-9856	35	8	-	-	ADJ
fcis-9856	35	9	resolution	resolution	ADJ
fcis-9856	35	10	reconstruction	reconstruction	NOUN
fcis-9856	35	11	of	of	ADP
fcis-9856	35	12	remote	remote	ADJ
fcis-9856	35	13	sensing	sensing	NOUN
fcis-9856	35	14	images	image	NOUN
fcis-9856	35	15	with	with	ADP
fcis-9856	35	16	unknown	unknown	ADJ
fcis-9856	35	17	degradation	degradation	NOUN
fcis-9856	35	18	model	model	NOUN
fcis-9856	35	19	,	,	PUNCT
fcis-9856	35	20	how	how	SCONJ
fcis-9856	35	21	to	to	PART
fcis-9856	35	22	accurately	accurately	ADV
fcis-9856	35	23	model	model	VERB
fcis-9856	35	24	the	the	DET
fcis-9856	35	25	degradation	degradation	NOUN
fcis-9856	35	26	model	model	NOUN
fcis-9856	35	27	using	use	VERB
fcis-9856	35	28	remote	remote	ADJ
fcis-9856	35	29	sensing	sense	VERB
fcis-9856	35	30	a	a	DET
fcis-9856	35	31	priori	priori	ADJ
fcis-9856	35	32	knowledge	knowledge	NOUN
fcis-9856	35	33	is	be	AUX
fcis-9856	35	34	the	the	DET
fcis-9856	35	35	key	key	NOUN
fcis-9856	35	36	to	to	PART
fcis-9856	35	37	enhance	enhance	VERB
fcis-9856	35	38	the	the	DET
fcis-9856	35	39	reconstruction	reconstruction	NOUN
fcis-9856	35	40	effect	effect	NOUN
fcis-9856	35	41	.	.	PUNCT
fcis-9856	36	1	in	in	ADP
fcis-9856	36	2	this	this	DET
fcis-9856	36	3	paper	paper	NOUN
fcis-9856	36	4	,	,	PUNCT
fcis-9856	36	5	we	we	PRON
fcis-9856	36	6	adopt	adopt	VERB
fcis-9856	36	7	a	a	DET
fcis-9856	36	8	random	random	ADJ
fcis-9856	36	9	blending	blend	VERB
fcis-9856	36	10	strategy	strategy	NOUN
fcis-9856	36	11	to	to	PART
fcis-9856	36	12	incorporate	incorporate	VERB
fcis-9856	36	13	possible	possible	ADJ
fcis-9856	36	14	degradation	degradation	NOUN
fcis-9856	36	15	factors	factor	NOUN
fcis-9856	36	16	(	(	PUNCT
fcis-9856	36	17	such	such	ADJ
fcis-9856	36	18	as	as	ADP
fcis-9856	36	19	blurring	blur	VERB
fcis-9856	36	20	,	,	PUNCT
fcis-9856	36	21	downsampling	downsampling	NOUN
fcis-9856	36	22	,	,	PUNCT
fcis-9856	36	23	noise	noise	NOUN
fcis-9856	36	24	,	,	PUNCT
fcis-9856	36	25	etc	etc	X
fcis-9856	36	26	.	.	X
fcis-9856	36	27	)	)	PUNCT
fcis-9856	37	1	in	in	ADP
fcis-9856	37	2	the	the	DET
fcis-9856	37	3	field	field	NOUN
fcis-9856	37	4	of	of	ADP
fcis-9856	37	5	remote	remote	ADJ
fcis-9856	37	6	sensing.[7	sensing.[7	ADJ
fcis-9856	37	7	]	]	X
fcis-9856	37	8	it	it	PRON
fcis-9856	37	9	is	be	AUX
fcis-9856	37	10	added	add	VERB
fcis-9856	37	11	to	to	ADP
fcis-9856	37	12	the	the	DET
fcis-9856	37	13	generation	generation	NOUN
fcis-9856	37	14	process	process	NOUN
fcis-9856	37	15	of	of	ADP
fcis-9856	37	16	low	low	ADJ
fcis-9856	37	17	-	-	PUNCT
fcis-9856	37	18	resolution	resolution	NOUN
fcis-9856	37	19	images	image	NOUN
fcis-9856	37	20	.	.	PUNCT
fcis-9856	38	1	and	and	CCONJ
fcis-9856	38	2	according	accord	VERB
fcis-9856	38	3	to	to	ADP
fcis-9856	38	4	the	the	DET
fcis-9856	38	5	characteristics	characteristic	NOUN
fcis-9856	38	6	of	of	ADP
fcis-9856	38	7	remote	remote	ADJ
fcis-9856	38	8	sensing	sense	VERB
fcis-9856	38	9	image	image	NOUN
fcis-9856	38	10	imaging	imaging	NOUN
fcis-9856	38	11	links	link	NOUN
fcis-9856	38	12	,	,	PUNCT
fcis-9856	38	13	some	some	DET
fcis-9856	38	14	degradation	degradation	NOUN
fcis-9856	38	15	factors	factor	NOUN
fcis-9856	38	16	(	(	PUNCT
fcis-9856	38	17	such	such	ADJ
fcis-9856	38	18	as	as	ADP
fcis-9856	38	19	isotropic	isotropic	ADJ
fcis-9856	38	20	gaussian	gaussian	ADJ
fcis-9856	38	21	blur	blur	NOUN
fcis-9856	38	22	kernel	kernel	PROPN
fcis-9856	38	23	,	,	PUNCT
fcis-9856	38	24	camera	camera	NOUN
fcis-9856	38	25	sensor	sensor	NOUN
fcis-9856	38	26	noise	noise	NOUN
fcis-9856	38	27	and	and	CCONJ
fcis-9856	38	28	jpeg	jpeg	NOUN
fcis-9856	38	29	compression	compression	NOUN
fcis-9856	38	30	noise	noise	NOUN
fcis-9856	38	31	)	)	PUNCT
fcis-9856	38	32	are	be	AUX
fcis-9856	38	33	excluded	exclude	VERB
fcis-9856	38	34	,	,	PUNCT
fcis-9856	38	35	and	and	CCONJ
fcis-9856	38	36	possible	possible	ADJ
fcis-9856	38	37	degradation	degradation	NOUN
fcis-9856	38	38	factors	factor	NOUN
fcis-9856	38	39	(	(	PUNCT
fcis-9856	38	40	such	such	ADJ
fcis-9856	38	41	as	as	ADP
fcis-9856	38	42	motion	motion	NOUN
fcis-9856	38	43	blur	blur	NOUN
fcis-9856	38	44	,	,	PUNCT
fcis-9856	38	45	additive	additive	ADJ
fcis-9856	38	46	noise	noise	NOUN
fcis-9856	38	47	and	and	CCONJ
fcis-9856	38	48	multiplicative	multiplicative	ADJ
fcis-9856	38	49	noise	noise	NOUN
fcis-9856	38	50	)	)	PUNCT
fcis-9856	38	51	in	in	ADP
fcis-9856	38	52	remote	remote	ADJ
fcis-9856	38	53	sensing	sense	VERB
fcis-9856	38	54	image	image	NOUN
fcis-9856	38	55	imaging	imaging	NOUN
fcis-9856	38	56	are	be	AUX
fcis-9856	38	57	added	add	VERB
fcis-9856	38	58	,	,	PUNCT
fcis-9856	38	59	so	so	SCONJ
fcis-9856	38	60	as	as	SCONJ
fcis-9856	38	61	to	to	PART
fcis-9856	38	62	ensure	ensure	VERB
fcis-9856	38	63	the	the	DET
fcis-9856	38	64	diversity	diversity	NOUN
fcis-9856	38	65	of	of	ADP
fcis-9856	38	66	the	the	DET
fcis-9856	38	67	synthesized	synthesize	VERB
fcis-9856	38	68	lowresolution	lowresolution	NOUN
fcis-9856	38	69	images	image	NOUN
fcis-9856	38	70	and	and	CCONJ
fcis-9856	38	71	reflect	reflect	VERB
fcis-9856	38	72	the	the	DET
fcis-9856	38	73	distribution	distribution	NOUN
fcis-9856	38	74	of	of	ADP
fcis-9856	38	75	real	real	ADJ
fcis-9856	38	76	image	image	NOUN
fcis-9856	38	77	features	feature	VERB
fcis-9856	38	78	as	as	ADV
fcis-9856	38	79	much	much	ADV
fcis-9856	38	80	as	as	ADP
fcis-9856	38	81	possible	possible	ADJ
fcis-9856	38	82	while	while	SCONJ
fcis-9856	38	83	avoiding	avoid	VERB
fcis-9856	38	84	synthesizing	synthesize	VERB
fcis-9856	38	85	lowresolution	lowresolution	NOUN
fcis-9856	38	86	images	image	NOUN
fcis-9856	38	87	that	that	PRON
fcis-9856	38	88	do	do	AUX
fcis-9856	38	89	not	not	PART
fcis-9856	38	90	match	match	VERB
fcis-9856	38	91	the	the	DET
fcis-9856	38	92	natural	natural	ADJ
fcis-9856	38	93	low	low	ADJ
fcis-9856	38	94	resolution	resolution	NOUN
fcis-9856	38	95	images	image	NOUN
fcis-9856	38	96	that	that	PRON
fcis-9856	38	97	do	do	AUX
fcis-9856	38	98	not	not	PART
fcis-9856	38	99	match	match	VERB
fcis-9856	38	100	at	at	ADV
fcis-9856	38	101	all	all	ADV
fcis-9856	38	102	in	in	ADP
fcis-9856	38	103	the	the	DET
fcis-9856	38	104	scene	scene	NOUN
fcis-9856	38	105	,	,	PUNCT
fcis-9856	38	106	reducing	reduce	VERB
fcis-9856	38	107	the	the	DET
fcis-9856	38	108	training	training	NOUN
fcis-9856	38	109	difficulty	difficulty	NOUN
fcis-9856	38	110	and	and	CCONJ
fcis-9856	38	111	speeding	speed	VERB
fcis-9856	38	112	up	up	ADP
fcis-9856	38	113	the	the	DET
fcis-9856	38	114	fitting	fitting	ADJ
fcis-9856	38	115	process	process	NOUN
fcis-9856	38	116	.	.	PUNCT
fcis-9856	39	1	the	the	DET
fcis-9856	39	2	degradation	degradation	NOUN
fcis-9856	39	3	process	process	NOUN
fcis-9856	39	4	modeled	model	VERB
fcis-9856	39	5	in	in	ADP
fcis-9856	39	6	this	this	DET
fcis-9856	39	7	paper	paper	NOUN
fcis-9856	39	8	consists	consist	VERB
fcis-9856	39	9	of	of	ADP
fcis-9856	39	10	three	three	NUM
fcis-9856	39	11	factors	factor	NOUN
fcis-9856	39	12	:	:	PUNCT
fcis-9856	39	13	blur	blur	NOUN
fcis-9856	39	14	,	,	PUNCT
fcis-9856	39	15	downsampling	downsampling	NOUN
fcis-9856	39	16	,	,	PUNCT
fcis-9856	39	17	and	and	CCONJ
fcis-9856	39	18	noise	noise	NOUN
fcis-9856	39	19	,	,	PUNCT
fcis-9856	39	20	and	and	CCONJ
fcis-9856	39	21	in	in	ADP
fcis-9856	39	22	the	the	DET
fcis-9856	39	23	actual	actual	ADJ
fcis-9856	39	24	imaging	imaging	NOUN
fcis-9856	39	25	process	process	NOUN
fcis-9856	39	26	.	.	PUNCT
fcis-9856	40	1	the	the	DET
fcis-9856	40	2	degradation	degradation	NOUN
fcis-9856	40	3	factors	factor	NOUN
fcis-9856	40	4	are	be	AUX
fcis-9856	40	5	disrupted	disrupt	VERB
fcis-9856	40	6	by	by	ADP
fcis-9856	40	7	using	use	VERB
fcis-9856	40	8	a	a	DET
fcis-9856	40	9	random	random	ADJ
fcis-9856	40	10	mixing	mixing	NOUN
fcis-9856	40	11	and	and	CCONJ
fcis-9856	40	12	washing	washing	NOUN
fcis-9856	40	13	strategy	strategy	NOUN
fcis-9856	40	14	,	,	PUNCT
fcis-9856	40	15	so	so	CCONJ
fcis-9856	40	16	the	the	DET
fcis-9856	40	17	order	order	NOUN
fcis-9856	40	18	of	of	ADP
fcis-9856	40	19	the	the	DET
fcis-9856	40	20	degradation	degradation	NOUN
fcis-9856	40	21	factors	factor	NOUN
fcis-9856	40	22	is	be	AUX
fcis-9856	40	23	uncertain	uncertain	ADJ
fcis-9856	40	24	.	.	PUNCT
fcis-9856	41	1	specifically	specifically	ADV
fcis-9856	41	2	,	,	PUNCT
fcis-9856	41	3	the	the	DET
fcis-9856	41	4	fuzzy	fuzzy	ADJ
fcis-9856	41	5	kernel	kernel	NOUN
fcis-9856	41	6	is	be	AUX
fcis-9856	41	7	randomly	randomly	ADV
fcis-9856	41	8	selected	select	VERB
fcis-9856	41	9	from	from	ADP
fcis-9856	41	10	the	the	DET
fcis-9856	41	11	anisotropic	anisotropic	NOUN
fcis-9856	41	12	gaussian	gaussian	ADJ
fcis-9856	41	13	fuzzy	fuzzy	ADJ
fcis-9856	41	14	kernel	kernel	NOUN
fcis-9856	41	15	,	,	PUNCT
fcis-9856	41	16	the	the	DET
fcis-9856	41	17	downsampling	downsampling	NOUN
fcis-9856	41	18	is	be	AUX
fcis-9856	41	19	randomly	randomly	ADV
fcis-9856	41	20	selected	select	VERB
fcis-9856	41	21	from	from	ADP
fcis-9856	41	22	the	the	DET
fcis-9856	41	23	nearest	near	ADJ
fcis-9856	41	24	neighbor	neighbor	NOUN
fcis-9856	41	25	sampling	sampling	NOUN
fcis-9856	41	26	,	,	PUNCT
fcis-9856	41	27	bilinear	bilinear	NOUN
fcis-9856	41	28	sampling	sampling	NOUN
fcis-9856	41	29	,	,	PUNCT
fcis-9856	41	30	and	and	CCONJ
fcis-9856	41	31	bicubic	bicubic	ADJ
fcis-9856	41	32	sampling	sampling	NOUN
fcis-9856	41	33	,	,	PUNCT
fcis-9856	41	34	and	and	CCONJ
fcis-9856	41	35	the	the	DET
fcis-9856	41	36	noise	noise	NOUN
fcis-9856	41	37	is	be	AUX
fcis-9856	41	38	randomly	randomly	ADV
fcis-9856	41	39	selected	select	VERB
fcis-9856	41	40	from	from	ADP
fcis-9856	41	41	the	the	DET
fcis-9856	41	42	additive	additive	ADJ
fcis-9856	41	43	noise	noise	NOUN
fcis-9856	41	44	and	and	CCONJ
fcis-9856	41	45	multiplicative	multiplicative	ADJ
fcis-9856	41	46	noise	noise	NOUN
fcis-9856	41	47	,	,	PUNCT
fcis-9856	41	48	and	and	CCONJ
fcis-9856	41	49	then	then	ADV
fcis-9856	41	50	these	these	DET
fcis-9856	41	51	three	three	NUM
fcis-9856	41	52	degradation	degradation	NOUN
fcis-9856	41	53	factors	factor	NOUN
fcis-9856	41	54	are	be	AUX
fcis-9856	41	55	randomly	randomly	ADV
fcis-9856	41	56	disordered	disorder	VERB
fcis-9856	41	57	to	to	PART
fcis-9856	41	58	obtain	obtain	VERB
fcis-9856	41	59	the	the	DET
fcis-9856	41	60	final	final	ADJ
fcis-9856	41	61	degradation	degradation	NOUN
fcis-9856	41	62	model	model	NOUN
fcis-9856	41	63	,	,	PUNCT
fcis-9856	41	64	as	as	SCONJ
fcis-9856	41	65	shown	show	VERB
fcis-9856	41	66	in	in	ADP
fcis-9856	41	67	figure	figure	NOUN
fcis-9856	41	68	2	2	NUM
fcis-9856	41	69	.	.	PUNCT
fcis-9856	41	70	anisotropic	anisotropic	NOUN
fcis-9856	41	71	gaussian	gaussian	ADJ
fcis-9856	41	72	fuzzy	fuzzy	ADJ
fcis-9856	41	73	kernel	kernel	PROPN
fcis-9856	41	74	nearest	near	ADJ
fcis-9856	41	75	neighbor	neighbor	PROPN
fcis-9856	41	76	interpolation	interpolation	NOUN
fcis-9856	41	77	bilinear	bilinear	NOUN
fcis-9856	41	78	interpolation	interpolation	NOUN
fcis-9856	41	79	bicubic	bicubic	NOUN
fcis-9856	41	80	interpolation	interpolation	NOUN
fcis-9856	41	81	additive	additive	NOUN
fcis-9856	41	82	noise	noise	NOUN
fcis-9856	41	83	multiplicative	multiplicative	ADJ
fcis-9856	41	84	noise	noise	NOUN
fcis-9856	41	85	random	random	ADJ
fcis-9856	41	86	random	random	ADJ
fcis-9856	41	87	random	random	ADJ
fcis-9856	41	88	hr	hr	NOUN
fcis-9856	41	89	image	image	NOUN
fcis-9856	41	90	input	input	NOUN
fcis-9856	41	91	lr	lr	NOUN
fcis-9856	41	92	image	image	NOUN
fcis-9856	41	93	output	output	NOUN
fcis-9856	41	94	figure	figure	NOUN
fcis-9856	41	95	2	2	NUM
fcis-9856	41	96	.	.	PUNCT
fcis-9856	41	97	degradation	degradation	NOUN
fcis-9856	41	98	model	model	NOUN
fcis-9856	41	99	2.1.1	2.1.1	NUM
fcis-9856	41	100	.	.	PUNCT
fcis-9856	42	1	blur	blur	VERB
fcis-9856	42	2	the	the	DET
fcis-9856	42	3	remote	remote	ADJ
fcis-9856	42	4	sensing	sense	VERB
fcis-9856	42	5	image	image	NOUN
fcis-9856	42	6	imaging	imaging	NOUN
fcis-9856	42	7	process	process	NOUN
fcis-9856	42	8	mainly	mainly	ADV
fcis-9856	42	9	contains	contain	VERB
fcis-9856	42	10	motion	motion	NOUN
fcis-9856	42	11	blur	blur	NOUN
fcis-9856	42	12	and	and	CCONJ
fcis-9856	42	13	optical	optical	ADJ
fcis-9856	42	14	blur	blur	PROPN
fcis-9856	42	15	.	.	PUNCT
fcis-9856	42	16	motion	motion	NOUN
fcis-9856	42	17	blur	blur	PROPN
fcis-9856	42	18	refers	refer	VERB
fcis-9856	42	19	to	to	ADP
fcis-9856	42	20	the	the	DET
fcis-9856	42	21	blurring	blur	VERB
fcis-9856	42	22	effect	effect	NOUN
fcis-9856	42	23	caused	cause	VERB
fcis-9856	42	24	by	by	ADP
fcis-9856	42	25	the	the	DET
fcis-9856	42	26	relative	relative	ADJ
fcis-9856	42	27	motion	motion	NOUN
fcis-9856	42	28	between	between	ADP
fcis-9856	42	29	the	the	DET
fcis-9856	42	30	sensor	sensor	NOUN
fcis-9856	42	31	and	and	CCONJ
fcis-9856	42	32	the	the	DET
fcis-9856	42	33	ground	ground	NOUN
fcis-9856	42	34	feature	feature	NOUN
fcis-9856	42	35	during	during	ADP
fcis-9856	42	36	the	the	DET
fcis-9856	42	37	satellite	satellite	NOUN
fcis-9856	42	38	motion	motion	NOUN
fcis-9856	42	39	and	and	CCONJ
fcis-9856	42	40	sensor	sensor	NOUN
fcis-9856	42	41	scanning	scanning	NOUN
fcis-9856	42	42	process	process	NOUN
fcis-9856	42	43	,	,	PUNCT
fcis-9856	42	44	which	which	PRON
fcis-9856	42	45	is	be	AUX
fcis-9856	42	46	influenced	influence	VERB
fcis-9856	42	47	by	by	ADP
fcis-9856	42	48	factors	factor	NOUN
fcis-9856	42	49	such	such	ADJ
fcis-9856	42	50	as	as	ADP
fcis-9856	42	51	angle	angle	NOUN
fcis-9856	42	52	and	and	CCONJ
fcis-9856	42	53	movement	movement	NOUN
fcis-9856	42	54	length	length	NOUN
fcis-9856	42	55	.	.	PUNCT
fcis-9856	43	1	when	when	SCONJ
fcis-9856	43	2	the	the	DET
fcis-9856	43	3	remote	remote	ADJ
fcis-9856	43	4	sensing	sense	VERB
fcis-9856	43	5	satellite	satellite	NOUN
fcis-9856	43	6	is	be	AUX
fcis-9856	43	7	imaging	imaging	NOUN
fcis-9856	43	8	,	,	PUNCT
fcis-9856	43	9	the	the	DET
fcis-9856	43	10	sensor	sensor	NOUN
fcis-9856	43	11	will	will	AUX
fcis-9856	43	12	receive	receive	VERB
fcis-9856	43	13	a	a	DET
fcis-9856	43	14	variety	variety	NOUN
fcis-9856	43	15	of	of	ADP
fcis-9856	43	16	radiation	radiation	NOUN
fcis-9856	43	17	such	such	ADJ
fcis-9856	43	18	as	as	ADP
fcis-9856	43	19	radiation	radiation	NOUN
fcis-9856	43	20	reflected	reflect	VERB
fcis-9856	43	21	directly	directly	ADV
fcis-9856	43	22	from	from	ADP
fcis-9856	43	23	the	the	DET
fcis-9856	43	24	surface	surface	NOUN
fcis-9856	43	25	features	feature	NOUN
fcis-9856	43	26	,	,	PUNCT
fcis-9856	43	27	radiation	radiation	NOUN
fcis-9856	43	28	reflected	reflect	VERB
fcis-9856	43	29	secondarily	secondarily	ADV
fcis-9856	43	30	by	by	ADP
fcis-9856	43	31	the	the	DET
fcis-9856	43	32	surface	surface	NOUN
fcis-9856	43	33	after	after	ADP
fcis-9856	43	34	downward	downward	ADJ
fcis-9856	43	35	scattering	scattering	NOUN
fcis-9856	43	36	from	from	ADP
fcis-9856	43	37	the	the	DET
fcis-9856	43	38	atmosphere	atmosphere	NOUN
fcis-9856	43	39	and	and	CCONJ
fcis-9856	43	40	the	the	DET
fcis-9856	43	41	part	part	NOUN
fcis-9856	43	42	of	of	ADP
fcis-9856	43	43	upward	upward	ADJ
fcis-9856	43	44	scattering	scatter	VERB
fcis-9856	43	45	from	from	ADP
fcis-9856	43	46	the	the	DET
fcis-9856	43	47	solar	solar	ADJ
fcis-9856	43	48	radiation	radiation	NOUN
fcis-9856	43	49	,	,	PUNCT
fcis-9856	43	50	which	which	PRON
fcis-9856	43	51	eventually	eventually	ADV
fcis-9856	43	52	leads	lead	VERB
fcis-9856	43	53	to	to	ADP
fcis-9856	43	54	the	the	DET
fcis-9856	43	55	scattered	scatter	VERB
fcis-9856	43	56	in	in	ADP
fcis-9856	43	57	the	the	DET
fcis-9856	43	58	actual	actual	ADJ
fcis-9856	43	59	imaging	imaging	NOUN
fcis-9856	43	60	process	process	NOUN
fcis-9856	43	61	,	,	PUNCT
fcis-9856	43	62	both	both	DET
fcis-9856	43	63	motion	motion	NOUN
fcis-9856	43	64	blur	blur	NOUN
fcis-9856	43	65	and	and	CCONJ
fcis-9856	43	66	optical	optical	ADJ
fcis-9856	43	67	blur	blur	NOUN
fcis-9856	43	68	may	may	AUX
fcis-9856	43	69	occur	occur	VERB
fcis-9856	43	70	,	,	PUNCT
fcis-9856	43	71	acting	act	VERB
fcis-9856	43	72	together	together	ADV
fcis-9856	43	73	to	to	PART
fcis-9856	43	74	form	form	VERB
fcis-9856	43	75	image	image	NOUN
fcis-9856	43	76	blur	blur	NOUN
fcis-9856	43	77	.	.	PUNCT
fcis-9856	44	1	in	in	ADP
fcis-9856	44	2	the	the	DET
fcis-9856	44	3	field	field	NOUN
fcis-9856	44	4	of	of	ADP
fcis-9856	44	5	super	super	ADJ
fcis-9856	44	6	-	-	ADJ
fcis-9856	44	7	resolution	resolution	ADJ
fcis-9856	44	8	reconstruction	reconstruction	NOUN
fcis-9856	44	9	,	,	PUNCT
fcis-9856	44	10	the	the	DET
fcis-9856	44	11	fuzzy	fuzzy	ADJ
fcis-9856	44	12	kernel	kernel	NOUN
fcis-9856	44	13	is	be	AUX
fcis-9856	44	14	modeled	model	VERB
fcis-9856	44	15	by	by	ADP
fcis-9856	44	16	the	the	DET
fcis-9856	44	17	zero	zero	NUM
fcis-9856	44	18	-	-	PUNCT
fcis-9856	44	19	mean	mean	NOUN
fcis-9856	44	20	gaussian	gaussian	ADJ
fcis-9856	44	21	probability	probability	NOUN
fcis-9856	44	22	distribution	distribution	NOUN
fcis-9856	44	23	function	function	NOUN
fcis-9856	44	24	n	n	CCONJ
fcis-9856	44	25	(	(	PUNCT
fcis-9856	44	26	0	0	NUM
fcis-9856	44	27	,	,	PUNCT
fcis-9856	44	28	σ	σ	PROPN
fcis-9856	44	29	)	)	PUNCT
fcis-9856	44	30	,	,	PUNCT
fcis-9856	44	31	and	and	CCONJ
fcis-9856	44	32	the	the	DET
fcis-9856	44	33	rotation	rotation	NOUN
fcis-9856	44	34	angle	angle	NOUN
fcis-9856	44	35	θ	θ	PROPN
fcis-9856	44	36	and	and	CCONJ
fcis-9856	44	37	the	the	DET
fcis-9856	44	38	eigenvalues	eigenvalue	NOUN
fcis-9856	44	39	of	of	ADP
fcis-9856	44	40	the	the	DET
fcis-9856	44	41	eigenvectors	eigenvector	NOUN
fcis-9856	44	42	of	of	ADP
fcis-9856	44	43	the	the	DET
fcis-9856	44	44	covariance	covariance	NOUN
fcis-9856	44	45	matrix	matrix	NOUN
fcis-9856	44	46	σ	σ	PUNCT
fcis-9856	44	47	jointly	jointly	ADV
fcis-9856	44	48	determine	determine	VERB
fcis-9856	44	49	the	the	DET
fcis-9856	44	50	shape	shape	NOUN
fcis-9856	44	51	of	of	ADP
fcis-9856	44	52	the	the	DET
fcis-9856	44	53	fuzzy	fuzzy	ADJ
fcis-9856	44	54	kernel	kernel	NOUN
fcis-9856	44	55	.	.	PUNCT
fcis-9856	45	1	in	in	ADP
fcis-9856	45	2	this	this	DET
fcis-9856	45	3	paper	paper	NOUN
fcis-9856	45	4	,	,	PUNCT
fcis-9856	45	5	the	the	DET
fcis-9856	45	6	rotation	rotation	NOUN
fcis-9856	45	7	angle	angle	NOUN
fcis-9856	45	8	θ	θ	PROPN
fcis-9856	45	9	is	be	AUX
fcis-9856	45	10	taken	take	VERB
fcis-9856	45	11	in	in	ADP
fcis-9856	45	12	the	the	DET
fcis-9856	45	13	range	range	NOUN
fcis-9856	45	14	of	of	ADP
fcis-9856	45	15	[	[	X
fcis-9856	45	16	0	0	NUM
fcis-9856	45	17	,	,	PUNCT
fcis-9856	45	18	π	π	NOUN
fcis-9856	45	19	]	]	X
fcis-9856	45	20	and	and	CCONJ
fcis-9856	45	21	the	the	DET
fcis-9856	45	22	eigenvalues	eigenvalue	NOUN
fcis-9856	45	23	l1	l1	PROPN
fcis-9856	45	24	,	,	PUNCT
fcis-9856	45	25	l2	l2	NOUN
fcis-9856	45	26	of	of	ADP
fcis-9856	45	27	the	the	DET
fcis-9856	45	28	eigenvectors	eigenvector	NOUN
fcis-9856	45	29	.	.	PUNCT
fcis-9856	46	1	are	be	AUX
fcis-9856	46	2	taken	take	VERB
fcis-9856	46	3	in	in	ADP
fcis-9856	46	4	the	the	DET
fcis-9856	46	5	range	range	NOUN
fcis-9856	46	6	of	of	ADP
fcis-9856	46	7	[	[	X
fcis-9856	46	8	0.2	0.2	NUM
fcis-9856	46	9	,	,	PUNCT
fcis-9856	46	10	8	8	NUM
fcis-9856	46	11	]	]	PUNCT
fcis-9856	46	12	,	,	PUNCT
fcis-9856	46	13	and	and	CCONJ
fcis-9856	46	14	when	when	SCONJ
fcis-9856	46	15	l1	l1	PROPN
fcis-9856	46	16	=	=	PUNCT
fcis-9856	46	17	l2	l2	PROPN
fcis-9856	46	18	,	,	PUNCT
fcis-9856	46	19	it	it	PRON
fcis-9856	46	20	is	be	AUX
fcis-9856	46	21	an	an	DET
fcis-9856	46	22	isotropic	isotropic	ADJ
fcis-9856	46	23	gaussian	gaussian	ADJ
fcis-9856	46	24	fuzzy	fuzzy	ADJ
fcis-9856	46	25	kernel	kernel	NOUN
fcis-9856	46	26	.	.	PUNCT
fcis-9856	47	1	the	the	DET
fcis-9856	47	2	joint	joint	ADJ
fcis-9856	47	3	effect	effect	NOUN
fcis-9856	47	4	of	of	ADP
fcis-9856	47	5	motion	motion	NOUN
fcis-9856	47	6	blur	blur	NOUN
fcis-9856	47	7	and	and	CCONJ
fcis-9856	47	8	optical	optical	ADJ
fcis-9856	47	9	blur	blur	NOUN
fcis-9856	47	10	in	in	ADP
fcis-9856	47	11	remote	remote	ADJ
fcis-9856	47	12	sensing	sense	VERB
fcis-9856	47	13	imaging	imaging	NOUN
fcis-9856	47	14	leads	lead	VERB
fcis-9856	47	15	to	to	ADP
fcis-9856	47	16	an	an	DET
fcis-9856	47	17	isotropic	isotropic	ADJ
fcis-9856	47	18	fuzzy	fuzzy	ADJ
fcis-9856	47	19	kernel	kernel	NOUN
fcis-9856	47	20	with	with	ADP
fcis-9856	47	21	very	very	ADV
fcis-9856	47	22	low	low	ADJ
fcis-9856	47	23	probability	probability	NOUN
fcis-9856	47	24	of	of	ADP
fcis-9856	47	25	occurrence	occurrence	NOUN
fcis-9856	47	26	and	and	CCONJ
fcis-9856	47	27	is	be	AUX
fcis-9856	47	28	no	no	PRON
fcis-9856	47	29	longer	long	ADV
fcis-9856	47	30	added	add	VERB
fcis-9856	47	31	separately	separately	ADV
fcis-9856	47	32	.	.	PUNCT
fcis-9856	48	1	2.1.2	2.1.2	X
fcis-9856	48	2	.	.	X
fcis-9856	48	3	downsampling	downsample	VERB
fcis-9856	48	4	in	in	ADP
fcis-9856	48	5	the	the	DET
fcis-9856	48	6	field	field	NOUN
fcis-9856	48	7	of	of	ADP
fcis-9856	48	8	super	super	ADJ
fcis-9856	48	9	-	-	ADJ
fcis-9856	48	10	resolution	resolution	ADJ
fcis-9856	48	11	reconstruction	reconstruction	NOUN
fcis-9856	48	12	of	of	ADP
fcis-9856	48	13	remote	remote	ADJ
fcis-9856	48	14	sensing	sensing	NOUN
fcis-9856	48	15	images	image	NOUN
fcis-9856	48	16	,	,	PUNCT
fcis-9856	48	17	the	the	DET
fcis-9856	48	18	bicubic	bicubic	NOUN
fcis-9856	48	19	interpolation	interpolation	NOUN
fcis-9856	48	20	method	method	NOUN
fcis-9856	48	21	is	be	AUX
fcis-9856	48	22	usually	usually	ADV
fcis-9856	48	23	used	use	VERB
fcis-9856	48	24	to	to	PART
fcis-9856	48	25	downsample	downsample	VERB
fcis-9856	48	26	the	the	DET
fcis-9856	48	27	high	high	ADJ
fcis-9856	48	28	-	-	PUNCT
fcis-9856	48	29	resolution	resolution	NOUN
fcis-9856	48	30	images	image	NOUN
fcis-9856	48	31	,	,	PUNCT
fcis-9856	48	32	which	which	PRON
fcis-9856	48	33	can	can	AUX
fcis-9856	48	34	simultaneously	simultaneously	ADV
fcis-9856	48	35	achieve	achieve	VERB
fcis-9856	48	36	the	the	DET
fcis-9856	48	37	requirements	requirement	NOUN
fcis-9856	48	38	of	of	ADP
fcis-9856	48	39	shortening	shorten	VERB
fcis-9856	48	40	the	the	DET
fcis-9856	48	41	processing	processing	NOUN
fcis-9856	48	42	time	time	NOUN
fcis-9856	48	43	and	and	CCONJ
fcis-9856	48	44	improving	improve	VERB
fcis-9856	48	45	the	the	DET
fcis-9856	48	46	image	image	NOUN
fcis-9856	48	47	quality	quality	NOUN
fcis-9856	48	48	.	.	PUNCT
fcis-9856	49	1	in	in	ADP
fcis-9856	49	2	order	order	NOUN
fcis-9856	49	3	to	to	PART
fcis-9856	49	4	enrich	enrich	VERB
fcis-9856	49	5	the	the	DET
fcis-9856	49	6	number	number	NOUN
fcis-9856	49	7	of	of	ADP
fcis-9856	49	8	samples	sample	NOUN
fcis-9856	49	9	for	for	ADP
fcis-9856	49	10	downsampling	downsample	VERB
fcis-9856	49	11	,	,	PUNCT
fcis-9856	49	12	in	in	ADP
fcis-9856	49	13	addition	addition	NOUN
fcis-9856	49	14	to	to	ADP
fcis-9856	49	15	bicubic	bicubic	ADJ
fcis-9856	49	16	interpolation	interpolation	NOUN
fcis-9856	49	17	,	,	PUNCT
fcis-9856	49	18	nearest	nearest	ADJ
fcis-9856	49	19	-	-	PUNCT
fcis-9856	49	20	neighbor	neighbor	NOUN
fcis-9856	49	21	interpolation	interpolation	NOUN
fcis-9856	49	22	and	and	CCONJ
fcis-9856	49	23	bilinear	bilinear	NOUN
fcis-9856	49	24	interpolation	interpolation	NOUN
fcis-9856	49	25	are	be	AUX
fcis-9856	49	26	also	also	ADV
fcis-9856	49	27	used	use	VERB
fcis-9856	49	28	in	in	ADP
fcis-9856	49	29	this	this	DET
fcis-9856	49	30	paper	paper	NOUN
fcis-9856	49	31	,	,	PUNCT
fcis-9856	49	32	and	and	CCONJ
fcis-9856	49	33	one	one	NUM
fcis-9856	49	34	of	of	ADP
fcis-9856	49	35	these	these	DET
fcis-9856	49	36	three	three	NUM
fcis-9856	49	37	interpolation	interpolation	NOUN
fcis-9856	49	38	methods	method	NOUN
fcis-9856	49	39	will	will	AUX
fcis-9856	49	40	be	be	AUX
fcis-9856	49	41	randomly	randomly	ADV
fcis-9856	49	42	selected	select	VERB
fcis-9856	49	43	in	in	ADP
fcis-9856	49	44	the	the	DET
fcis-9856	49	45	downsampling	downsample	VERB
fcis-9856	49	46	operation	operation	NOUN
fcis-9856	49	47	.	.	PUNCT
fcis-9856	50	1	33	33	NUM
fcis-9856	50	2	2.1.3	2.1.3	NUM
fcis-9856	50	3	.	.	PUNCT
fcis-9856	50	4	noise	noise	NOUN
fcis-9856	50	5	noise	noise	NOUN
fcis-9856	50	6	in	in	ADP
fcis-9856	50	7	remote	remote	ADJ
fcis-9856	50	8	sensing	sensing	NOUN
fcis-9856	50	9	images	image	NOUN
fcis-9856	50	10	may	may	AUX
fcis-9856	50	11	be	be	AUX
fcis-9856	50	12	introduced	introduce	VERB
fcis-9856	50	13	by	by	ADP
fcis-9856	50	14	different	different	ADJ
fcis-9856	50	15	processing	processing	NOUN
fcis-9856	50	16	processes	process	NOUN
fcis-9856	50	17	at	at	ADP
fcis-9856	50	18	different	different	ADJ
fcis-9856	50	19	stages	stage	NOUN
fcis-9856	50	20	,	,	PUNCT
fcis-9856	50	21	including	include	VERB
fcis-9856	50	22	acquisition	acquisition	NOUN
fcis-9856	50	23	signal	signal	NOUN
fcis-9856	50	24	,	,	PUNCT
fcis-9856	50	25	acquisition	acquisition	NOUN
fcis-9856	50	26	noise	noise	NOUN
fcis-9856	50	27	,	,	PUNCT
fcis-9856	50	28	and	and	CCONJ
fcis-9856	50	29	internal	internal	ADJ
fcis-9856	50	30	sensor	sensor	NOUN
fcis-9856	50	31	circuit	circuit	NOUN
fcis-9856	50	32	noise	noise	NOUN
fcis-9856	50	33	.	.	PUNCT
fcis-9856	51	1	the	the	DET
fcis-9856	51	2	imaging	imaging	NOUN
fcis-9856	51	3	noise	noise	NOUN
fcis-9856	51	4	varies	vary	VERB
fcis-9856	51	5	with	with	ADP
fcis-9856	51	6	different	different	ADJ
fcis-9856	51	7	sensor	sensor	NOUN
fcis-9856	51	8	types	type	NOUN
fcis-9856	51	9	.	.	PUNCT
fcis-9856	52	1	in	in	ADP
fcis-9856	52	2	this	this	DET
fcis-9856	52	3	paper	paper	NOUN
fcis-9856	52	4	,	,	PUNCT
fcis-9856	52	5	we	we	PRON
fcis-9856	52	6	mainly	mainly	ADV
fcis-9856	52	7	consider	consider	VERB
fcis-9856	52	8	two	two	NUM
fcis-9856	52	9	noise	noise	NOUN
fcis-9856	52	10	models	model	NOUN
fcis-9856	52	11	:	:	PUNCT
fcis-9856	52	12	additive	additive	ADJ
fcis-9856	52	13	noise	noise	NOUN
fcis-9856	52	14	model	model	NOUN
fcis-9856	52	15	in	in	ADP
fcis-9856	52	16	optical	optical	ADJ
fcis-9856	52	17	imaging	imaging	NOUN
fcis-9856	52	18	system	system	NOUN
fcis-9856	52	19	and	and	CCONJ
fcis-9856	52	20	multiplicative	multiplicative	ADJ
fcis-9856	52	21	noise	noise	NOUN
fcis-9856	52	22	model	model	NOUN
fcis-9856	52	23	in	in	ADP
fcis-9856	52	24	remote	remote	ADJ
fcis-9856	52	25	sensing	sense	VERB
fcis-9856	52	26	imaging	imaging	NOUN
fcis-9856	52	27	system.[8	system.[8	NOUN
fcis-9856	52	28	]	]	PUNCT
fcis-9856	52	29	2.2	2.2	NUM
fcis-9856	52	30	.	.	PUNCT
fcis-9856	53	1	generative	generative	ADJ
fcis-9856	53	2	adversarial	adversarial	ADJ
fcis-9856	53	3	networks	network	NOUN
fcis-9856	53	4	remote	remote	ADJ
fcis-9856	53	5	sensing	sense	VERB
fcis-9856	53	6	images	image	NOUN
fcis-9856	53	7	contain	contain	VERB
fcis-9856	53	8	rich	rich	ADJ
fcis-9856	53	9	information	information	NOUN
fcis-9856	53	10	of	of	ADP
fcis-9856	53	11	landforms	landform	NOUN
fcis-9856	53	12	and	and	CCONJ
fcis-9856	53	13	store	store	VERB
fcis-9856	53	14	a	a	DET
fcis-9856	53	15	large	large	ADJ
fcis-9856	53	16	amount	amount	NOUN
fcis-9856	53	17	of	of	ADP
fcis-9856	53	18	information	information	NOUN
fcis-9856	53	19	of	of	ADP
fcis-9856	53	20	cities	city	NOUN
fcis-9856	53	21	,	,	PUNCT
fcis-9856	53	22	roads	road	NOUN
fcis-9856	53	23	,	,	PUNCT
fcis-9856	53	24	bridges	bridge	NOUN
fcis-9856	53	25	,	,	PUNCT
fcis-9856	53	26	etc	etc	X
fcis-9856	53	27	.	.	X
fcis-9856	54	1	within	within	ADP
fcis-9856	54	2	smaller	small	ADJ
fcis-9856	54	3	pixels	pixel	NOUN
fcis-9856	54	4	,	,	PUNCT
fcis-9856	54	5	which	which	PRON
fcis-9856	54	6	require	require	VERB
fcis-9856	54	7	high	high	ADJ
fcis-9856	54	8	recovery	recovery	NOUN
fcis-9856	54	9	of	of	ADP
fcis-9856	54	10	texture	texture	NOUN
fcis-9856	54	11	and	and	CCONJ
fcis-9856	54	12	level	level	NOUN
fcis-9856	54	13	details	detail	NOUN
fcis-9856	54	14	of	of	ADP
fcis-9856	54	15	images	image	NOUN
fcis-9856	54	16	.	.	PUNCT
fcis-9856	55	1	in	in	ADP
fcis-9856	55	2	the	the	DET
fcis-9856	55	3	traditional	traditional	ADJ
fcis-9856	55	4	method	method	NOUN
fcis-9856	55	5	of	of	ADP
fcis-9856	55	6	convolutional	convolutional	ADJ
fcis-9856	55	7	neural	neural	ADJ
fcis-9856	55	8	network	network	NOUN
fcis-9856	55	9	cnn	cnn	PROPN
fcis-9856	55	10	,	,	PUNCT
fcis-9856	55	11	the	the	DET
fcis-9856	55	12	perceptual	perceptual	ADJ
fcis-9856	55	13	field	field	NOUN
fcis-9856	55	14	of	of	ADP
fcis-9856	55	15	the	the	DET
fcis-9856	55	16	convolutional	convolutional	ADJ
fcis-9856	55	17	neural	neural	ADJ
fcis-9856	55	18	network	network	NOUN
fcis-9856	55	19	is	be	AUX
fcis-9856	55	20	determined	determine	VERB
fcis-9856	55	21	by	by	ADP
fcis-9856	55	22	the	the	DET
fcis-9856	55	23	size	size	NOUN
fcis-9856	55	24	of	of	ADP
fcis-9856	55	25	the	the	DET
fcis-9856	55	26	convolutional	convolutional	ADJ
fcis-9856	55	27	kernel	kernel	NOUN
fcis-9856	55	28	.	.	PUNCT
fcis-9856	56	1	all	all	DET
fcis-9856	56	2	the	the	DET
fcis-9856	56	3	information	information	NOUN
fcis-9856	56	4	within	within	ADP
fcis-9856	56	5	the	the	DET
fcis-9856	56	6	convolutional	convolutional	ADJ
fcis-9856	56	7	kernel	kernel	NOUN
fcis-9856	56	8	is	be	AUX
fcis-9856	56	9	equally	equally	ADV
fcis-9856	56	10	attended	attend	VERB
fcis-9856	56	11	to	to	ADP
fcis-9856	56	12	,	,	PUNCT
fcis-9856	56	13	making	make	VERB
fcis-9856	56	14	the	the	DET
fcis-9856	56	15	loss	loss	NOUN
fcis-9856	56	16	in	in	ADP
fcis-9856	56	17	the	the	DET
fcis-9856	56	18	recovery	recovery	NOUN
fcis-9856	56	19	process	process	NOUN
fcis-9856	56	20	smaller	small	ADJ
fcis-9856	56	21	in	in	ADP
fcis-9856	56	22	planar	planar	ADJ
fcis-9856	56	23	regions	region	NOUN
fcis-9856	56	24	,	,	PUNCT
fcis-9856	56	25	but	but	CCONJ
fcis-9856	56	26	the	the	DET
fcis-9856	56	27	loss	loss	NOUN
fcis-9856	56	28	is	be	AUX
fcis-9856	56	29	larger	large	ADJ
fcis-9856	56	30	in	in	ADP
fcis-9856	56	31	the	the	DET
fcis-9856	56	32	recovery	recovery	NOUN
fcis-9856	56	33	process	process	NOUN
fcis-9856	56	34	of	of	ADP
fcis-9856	56	35	non	non	ADJ
fcis-9856	56	36	-	-	ADJ
fcis-9856	56	37	planar	planar	ADJ
fcis-9856	56	38	regions	region	NOUN
fcis-9856	56	39	such	such	ADJ
fcis-9856	56	40	as	as	ADP
fcis-9856	56	41	bridges	bridge	NOUN
fcis-9856	56	42	and	and	CCONJ
fcis-9856	56	43	building	building	NOUN
fcis-9856	56	44	clusters	cluster	NOUN
fcis-9856	56	45	,	,	PUNCT
fcis-9856	56	46	which	which	PRON
fcis-9856	56	47	may	may	AUX
fcis-9856	56	48	lead	lead	VERB
fcis-9856	56	49	to	to	ADP
fcis-9856	56	50	a	a	DET
fcis-9856	56	51	huge	huge	ADJ
fcis-9856	56	52	difference	difference	NOUN
fcis-9856	56	53	between	between	ADP
fcis-9856	56	54	the	the	DET
fcis-9856	56	55	recovered	recovered	ADJ
fcis-9856	56	56	image	image	NOUN
fcis-9856	56	57	and	and	CCONJ
fcis-9856	56	58	the	the	DET
fcis-9856	56	59	original	original	ADJ
fcis-9856	56	60	image	image	NOUN
fcis-9856	56	61	,	,	PUNCT
fcis-9856	56	62	resulting	result	VERB
fcis-9856	56	63	in	in	ADP
fcis-9856	56	64	information	information	NOUN
fcis-9856	56	65	misalignment	misalignment	NOUN
fcis-9856	56	66	.	.	PUNCT
fcis-9856	57	1	in	in	ADP
fcis-9856	57	2	this	this	DET
fcis-9856	57	3	paper	paper	NOUN
fcis-9856	57	4	,	,	PUNCT
fcis-9856	57	5	an	an	DET
fcis-9856	57	6	image	image	NOUN
fcis-9856	57	7	super	super	ADJ
fcis-9856	57	8	-	-	ADJ
fcis-9856	57	9	resolution	resolution	ADJ
fcis-9856	57	10	method	method	NOUN
fcis-9856	57	11	based	base	VERB
fcis-9856	57	12	on	on	ADP
fcis-9856	57	13	srgan	srgan	NOUN
fcis-9856	57	14	is	be	AUX
fcis-9856	57	15	proposed	propose	VERB
fcis-9856	57	16	,	,	PUNCT
fcis-9856	57	17	and	and	CCONJ
fcis-9856	57	18	its	its	PRON
fcis-9856	57	19	generator	generator	NOUN
fcis-9856	57	20	architecture	architecture	NOUN
fcis-9856	57	21	is	be	AUX
fcis-9856	57	22	modified	modify	VERB
fcis-9856	57	23	in	in	ADP
fcis-9856	57	24	the	the	DET
fcis-9856	57	25	proposed	propose	VERB
fcis-9856	57	26	method	method	NOUN
fcis-9856	57	27	.	.	PUNCT
fcis-9856	58	1	2.2.1	2.2.1	NUM
fcis-9856	58	2	.	.	PUNCT
fcis-9856	58	3	generate	generate	VERB
fcis-9856	58	4	network	network	NOUN
fcis-9856	58	5	in	in	ADP
fcis-9856	58	6	order	order	NOUN
fcis-9856	58	7	to	to	PART
fcis-9856	58	8	overcome	overcome	VERB
fcis-9856	58	9	the	the	DET
fcis-9856	58	10	drawbacks	drawback	NOUN
fcis-9856	58	11	of	of	ADP
fcis-9856	58	12	srgan	srgan	NOUN
fcis-9856	58	13	and	and	CCONJ
fcis-9856	58	14	to	to	PART
fcis-9856	58	15	improve	improve	VERB
fcis-9856	58	16	its	its	PRON
fcis-9856	58	17	image	image	NOUN
fcis-9856	58	18	recovery	recovery	NOUN
fcis-9856	58	19	accuracy	accuracy	NOUN
fcis-9856	58	20	,	,	PUNCT
fcis-9856	58	21	a	a	DET
fcis-9856	58	22	novel	novel	ADJ
fcis-9856	58	23	generator	generator	NOUN
fcis-9856	58	24	network	network	NOUN
fcis-9856	58	25	with	with	ADP
fcis-9856	58	26	the	the	DET
fcis-9856	58	27	following	follow	VERB
fcis-9856	58	28	three	three	NUM
fcis-9856	58	29	main	main	ADJ
fcis-9856	58	30	changes	change	NOUN
fcis-9856	58	31	is	be	AUX
fcis-9856	58	32	proposed	propose	VERB
fcis-9856	58	33	in	in	ADP
fcis-9856	58	34	this	this	DET
fcis-9856	58	35	paper	paper	NOUN
fcis-9856	58	36	.	.	PUNCT
fcis-9856	59	1	a	a	DET
fcis-9856	59	2	block	block	NOUN
fcis-9856	59	3	diagram	diagram	NOUN
fcis-9856	59	4	of	of	ADP
fcis-9856	59	5	the	the	DET
fcis-9856	59	6	proposed	propose	VERB
fcis-9856	59	7	architecture	architecture	NOUN
fcis-9856	59	8	illustrating	illustrate	VERB
fcis-9856	59	9	the	the	DET
fcis-9856	59	10	changes	change	NOUN
fcis-9856	59	11	is	be	AUX
fcis-9856	59	12	shown	show	VERB
fcis-9856	59	13	in	in	ADP
fcis-9856	59	14	figure	figure	NOUN
fcis-9856	59	15	4	4	NUM
fcis-9856	59	16	.	.	PUNCT
fcis-9856	60	1	it	it	PRON
fcis-9856	60	2	consists	consist	VERB
fcis-9856	60	3	of	of	ADP
fcis-9856	60	4	two	two	NUM
fcis-9856	60	5	main	main	ADJ
fcis-9856	60	6	parts	part	NOUN
fcis-9856	60	7	:	:	PUNCT
fcis-9856	60	8	depth	depth	NOUN
fcis-9856	60	9	feature	feature	NOUN
fcis-9856	60	10	extraction	extraction	NOUN
fcis-9856	60	11	with	with	ADP
fcis-9856	60	12	2	2	NUM
fcis-9856	60	13	-	-	ADJ
fcis-9856	60	14	fold	fold	ADJ
fcis-9856	60	15	amplification	amplification	NOUN
fcis-9856	60	16	,	,	PUNCT
fcis-9856	60	17	enhanced	enhance	VERB
fcis-9856	60	18	feature	feature	NOUN
fcis-9856	60	19	extraction	extraction	NOUN
fcis-9856	60	20	and	and	CCONJ
fcis-9856	60	21	2	2	NUM
fcis-9856	60	22	-	-	ADJ
fcis-9856	60	23	fold	fold	ADJ
fcis-9856	60	24	enhanced	enhanced	ADJ
fcis-9856	60	25	extraction	extraction	NOUN
fcis-9856	60	26	.	.	PUNCT
fcis-9856	61	1	double	double	ADJ
fcis-9856	61	2	up	up	ADV
fcis-9856	61	3	-	-	PUNCT
fcis-9856	61	4	sampling	sample	VERB
fcis-9856	61	5	part	part	NOUN
fcis-9856	61	6	2	2	NUM
fcis-9856	61	7	double	double	ADJ
fcis-9856	61	8	up	up	ADP
fcis-9856	61	9	-	-	PUNCT
fcis-9856	61	10	samplingpart	samplingpart	NOUN
fcis-9856	61	11	1	1	NUM
fcis-9856	61	12	shallow	shallow	ADJ
fcis-9856	61	13	feature	feature	NOUN
fcis-9856	61	14	extraction	extraction	NOUN
fcis-9856	61	15	lr	lr	X
fcis-9856	61	16	input	input	NOUN
fcis-9856	61	17	resnet34	resnet34	NOUN
fcis-9856	61	18	deep	deep	ADJ
fcis-9856	61	19	feature	feature	NOUN
fcis-9856	61	20	extraction	extraction	NOUN
fcis-9856	61	21	up	up	ADP
fcis-9856	61	22	-	-	PUNCT
fcis-9856	61	23	sampling	sample	VERB
fcis-9856	61	24	image	image	NOUN
fcis-9856	61	25	feature	feature	NOUN
fcis-9856	61	26	extraction	extraction	NOUN
fcis-9856	61	27	+	+	NOUN
fcis-9856	61	28	convolution	convolution	NOUN
fcis-9856	61	29	hr	hr	NOUN
fcis-9856	61	30	output	output	NOUN
fcis-9856	61	31	part	part	NOUN
fcis-9856	61	32	3part	3part	NUM
fcis-9856	61	33	4	4	NUM
fcis-9856	61	34	figure	figure	NOUN
fcis-9856	61	35	3	3	NUM
fcis-9856	61	36	.	.	PUNCT
fcis-9856	61	37	general	general	ADJ
fcis-9856	61	38	flow	flow	NOUN
fcis-9856	61	39	chart	chart	NOUN
fcis-9856	61	40	of	of	ADP
fcis-9856	61	41	the	the	DET
fcis-9856	61	42	algorithm	algorithm	NOUN
fcis-9856	61	43	2.2.2	2.2.2	NUM
fcis-9856	61	44	.	.	PUNCT
fcis-9856	61	45	shallow	shallow	ADJ
fcis-9856	61	46	feature	feature	NOUN
fcis-9856	61	47	extraction	extraction	NOUN
fcis-9856	61	48	in	in	ADP
fcis-9856	61	49	this	this	DET
fcis-9856	61	50	part	part	NOUN
fcis-9856	61	51	of	of	ADP
fcis-9856	61	52	the	the	DET
fcis-9856	61	53	architecture	architecture	NOUN
fcis-9856	61	54	,	,	PUNCT
fcis-9856	61	55	the	the	DET
fcis-9856	61	56	lr	lr	PROPN
fcis-9856	61	57	version	version	NOUN
fcis-9856	61	58	of	of	ADP
fcis-9856	61	59	the	the	DET
fcis-9856	61	60	original	original	ADJ
fcis-9856	61	61	hr	hr	NOUN
fcis-9856	61	62	image	image	NOUN
fcis-9856	61	63	is	be	AUX
fcis-9856	61	64	used	use	VERB
fcis-9856	61	65	as	as	ADP
fcis-9856	61	66	the	the	DET
fcis-9856	61	67	input	input	NOUN
fcis-9856	61	68	to	to	ADP
fcis-9856	61	69	the	the	DET
fcis-9856	61	70	architecture	architecture	NOUN
fcis-9856	61	71	.	.	PUNCT
fcis-9856	62	1	and	and	CCONJ
fcis-9856	62	2	basic	basic	ADJ
fcis-9856	62	3	or	or	CCONJ
fcis-9856	62	4	shallow	shallow	ADJ
fcis-9856	62	5	features	feature	NOUN
fcis-9856	62	6	are	be	AUX
fcis-9856	62	7	computed	compute	VERB
fcis-9856	62	8	at	at	ADP
fcis-9856	62	9	three	three	NUM
fcis-9856	62	10	different	different	ADJ
fcis-9856	62	11	scales	scale	NOUN
fcis-9856	62	12	using	use	VERB
fcis-9856	62	13	kernels	kernel	NOUN
fcis-9856	62	14	of	of	ADP
fcis-9856	62	15	sizes	size	NOUN
fcis-9856	62	16	3	3	NUM
fcis-9856	62	17	,	,	PUNCT
fcis-9856	62	18	5	5	NUM
fcis-9856	62	19	and	and	CCONJ
fcis-9856	62	20	7	7	NUM
fcis-9856	62	21	,	,	PUNCT
fcis-9856	62	22	respectively	respectively	ADV
fcis-9856	62	23	.	.	PUNCT
fcis-9856	63	1	for	for	ADP
fcis-9856	63	2	feature	feature	NOUN
fcis-9856	63	3	extraction	extraction	NOUN
fcis-9856	63	4	at	at	ADP
fcis-9856	63	5	each	each	DET
fcis-9856	63	6	scale	scale	NOUN
fcis-9856	63	7	,	,	PUNCT
fcis-9856	63	8	two	two	NUM
fcis-9856	63	9	blocks	block	NOUN
fcis-9856	63	10	are	be	AUX
fcis-9856	63	11	used	use	VERB
fcis-9856	63	12	.	.	PUNCT
fcis-9856	64	1	each	each	DET
fcis-9856	64	2	block	block	NOUN
fcis-9856	64	3	consists	consist	VERB
fcis-9856	64	4	of	of	ADP
fcis-9856	64	5	two	two	NUM
fcis-9856	64	6	convolutional	convolutional	ADJ
fcis-9856	64	7	layers	layer	NOUN
fcis-9856	64	8	,	,	PUNCT
fcis-9856	64	9	the	the	DET
fcis-9856	64	10	first	first	ADJ
fcis-9856	64	11	convolutional	convolutional	ADJ
fcis-9856	64	12	layer	layer	NOUN
fcis-9856	64	13	in	in	ADP
fcis-9856	64	14	each	each	DET
fcis-9856	64	15	branch	branch	NOUN
fcis-9856	64	16	is	be	AUX
fcis-9856	64	17	followed	follow	VERB
fcis-9856	64	18	by	by	ADP
fcis-9856	64	19	a	a	DET
fcis-9856	64	20	batch	batch	NOUN
fcis-9856	64	21	normalization	normalization	NOUN
fcis-9856	64	22	layer	layer	NOUN
fcis-9856	64	23	and	and	CCONJ
fcis-9856	64	24	relu	relu	NOUN
fcis-9856	64	25	activation	activation	NOUN
fcis-9856	64	26	,	,	PUNCT
fcis-9856	64	27	and	and	CCONJ
fcis-9856	64	28	after	after	SCONJ
fcis-9856	64	29	the	the	DET
fcis-9856	64	30	second	second	ADJ
fcis-9856	64	31	convolutional	convolutional	ADJ
fcis-9856	64	32	layer	layer	NOUN
fcis-9856	64	33	is	be	AUX
fcis-9856	64	34	a	a	DET
fcis-9856	64	35	batch	batch	NOUN
fcis-9856	64	36	normalization	normalization	NOUN
fcis-9856	64	37	layer	layer	NOUN
fcis-9856	64	38	with	with	ADP
fcis-9856	64	39	64	64	NUM
fcis-9856	64	40	channels	channel	NOUN
fcis-9856	64	41	per	per	ADP
fcis-9856	64	42	convolutional	convolutional	ADJ
fcis-9856	64	43	layer	layer	NOUN
fcis-9856	64	44	.	.	PUNCT
fcis-9856	65	1	a	a	DET
fcis-9856	65	2	jump	jump	NOUN
fcis-9856	65	3	connection	connection	NOUN
fcis-9856	65	4	is	be	AUX
fcis-9856	65	5	used	use	VERB
fcis-9856	65	6	between	between	ADP
fcis-9856	65	7	the	the	DET
fcis-9856	65	8	outputs	output	NOUN
fcis-9856	65	9	of	of	ADP
fcis-9856	65	10	block	block	NOUN
fcis-9856	65	11	1	1	NUM
fcis-9856	65	12	and	and	CCONJ
fcis-9856	65	13	block	block	NOUN
fcis-9856	65	14	2	2	NUM
fcis-9856	65	15	,	,	PUNCT
fcis-9856	65	16	and	and	CCONJ
fcis-9856	65	17	the	the	DET
fcis-9856	65	18	features	feature	NOUN
fcis-9856	65	19	of	of	ADP
fcis-9856	65	20	block	block	NOUN
fcis-9856	65	21	1	1	NUM
fcis-9856	65	22	will	will	AUX
fcis-9856	65	23	be	be	AUX
fcis-9856	65	24	superimposed	superimpose	VERB
fcis-9856	65	25	feature	feature	NOUN
fcis-9856	65	26	by	by	ADP
fcis-9856	65	27	feature	feature	NOUN
fcis-9856	65	28	with	with	ADP
fcis-9856	65	29	those	those	PRON
fcis-9856	65	30	of	of	ADP
fcis-9856	65	31	block	block	NOUN
fcis-9856	65	32	2	2	NUM
fcis-9856	65	33	.	.	PUNCT
fcis-9856	66	1	after	after	ADP
fcis-9856	66	2	feature	feature	NOUN
fcis-9856	66	3	extraction	extraction	NOUN
fcis-9856	66	4	at	at	ADP
fcis-9856	66	5	different	different	ADJ
fcis-9856	66	6	scales	scale	NOUN
fcis-9856	66	7	,	,	PUNCT
fcis-9856	66	8	features	feature	NOUN
fcis-9856	66	9	from	from	ADP
fcis-9856	66	10	all	all	DET
fcis-9856	66	11	three	three	NUM
fcis-9856	66	12	scales	scale	NOUN
fcis-9856	66	13	are	be	AUX
fcis-9856	66	14	concatenated	concatenate	VERB
fcis-9856	66	15	(	(	PUNCT
fcis-9856	66	16	by	by	ADP
fcis-9856	66	17	channel	channel	NOUN
fcis-9856	66	18	)	)	PUNCT
fcis-9856	66	19	into	into	ADP
fcis-9856	66	20	a	a	DET
fcis-9856	66	21	single	single	ADJ
fcis-9856	66	22	feature	feature	NOUN
fcis-9856	66	23	vector	vector	NOUN
fcis-9856	66	24	to	to	PART
fcis-9856	66	25	form	form	VERB
fcis-9856	66	26	a	a	DET
fcis-9856	66	27	192	192	NUM
fcis-9856	66	28	-	-	PUNCT
fcis-9856	66	29	channel	channel	NOUN
fcis-9856	66	30	wide	wide	ADJ
fcis-9856	66	31	feature	feature	NOUN
fcis-9856	66	32	vector	vector	NOUN
fcis-9856	66	33	.	.	PUNCT
fcis-9856	67	1	this	this	DET
fcis-9856	67	2	feature	feature	NOUN
fcis-9856	67	3	vector	vector	NOUN
fcis-9856	67	4	contains	contain	VERB
fcis-9856	67	5	the	the	DET
fcis-9856	67	6	basic	basic	ADJ
fcis-9856	67	7	or	or	CCONJ
fcis-9856	67	8	shallow	shallow	ADJ
fcis-9856	67	9	features	feature	NOUN
fcis-9856	67	10	of	of	ADP
fcis-9856	67	11	the	the	DET
fcis-9856	67	12	lr	lr	NOUN
fcis-9856	67	13	image	image	NOUN
fcis-9856	67	14	and	and	CCONJ
fcis-9856	67	15	serves	serve	VERB
fcis-9856	67	16	as	as	ADP
fcis-9856	67	17	the	the	DET
fcis-9856	67	18	input	input	NOUN
fcis-9856	67	19	for	for	ADP
fcis-9856	67	20	the	the	DET
fcis-9856	67	21	next	next	ADJ
fcis-9856	67	22	step	step	NOUN
fcis-9856	67	23	of	of	ADP
fcis-9856	67	24	the	the	DET
fcis-9856	67	25	architecture	architecture	NOUN
fcis-9856	67	26	.	.	PUNCT
fcis-9856	68	1	2.2.3	2.2.3	X
fcis-9856	68	2	.	.	X
fcis-9856	68	3	deep	deep	ADJ
fcis-9856	68	4	feature	feature	NOUN
fcis-9856	68	5	extraction	extraction	NOUN
fcis-9856	68	6	and	and	CCONJ
fcis-9856	68	7	2x	2x	NUM
fcis-9856	68	8	zoom	zoom	VERB
fcis-9856	68	9	in	in	ADP
fcis-9856	68	10	this	this	DET
fcis-9856	68	11	paper	paper	NOUN
fcis-9856	68	12	,	,	PUNCT
fcis-9856	68	13	192	192	NUM
fcis-9856	68	14	channels	channel	NOUN
fcis-9856	68	15	are	be	AUX
fcis-9856	68	16	used	use	VERB
fcis-9856	68	17	in	in	ADP
fcis-9856	68	18	each	each	DET
fcis-9856	68	19	convolutional	convolutional	ADJ
fcis-9856	68	20	layer	layer	NOUN
fcis-9856	68	21	.	.	PUNCT
fcis-9856	69	1	the	the	DET
fcis-9856	69	2	modified	modify	VERB
fcis-9856	69	3	resnet34	resnet34	NOUN
fcis-9856	69	4	architecture	architecture	NOUN
fcis-9856	69	5	consists	consist	VERB
fcis-9856	69	6	of	of	ADP
fcis-9856	69	7	17	17	NUM
fcis-9856	69	8	blocks	block	NOUN
fcis-9856	69	9	,	,	PUNCT
fcis-9856	69	10	each	each	DET
fcis-9856	69	11	block	block	NOUN
fcis-9856	69	12	containing	contain	VERB
fcis-9856	69	13	two	two	NUM
fcis-9856	69	14	convolutional	convolutional	ADJ
fcis-9856	69	15	layers	layer	NOUN
fcis-9856	69	16	.	.	PUNCT
fcis-9856	70	1	the	the	DET
fcis-9856	70	2	first	first	ADJ
fcis-9856	70	3	convolutional	convolutional	ADJ
fcis-9856	70	4	layer	layer	NOUN
fcis-9856	70	5	is	be	AUX
fcis-9856	70	6	followed	follow	VERB
fcis-9856	70	7	by	by	ADP
fcis-9856	70	8	a	a	DET
fcis-9856	70	9	batch	batch	NOUN
fcis-9856	70	10	normalization	normalization	NOUN
fcis-9856	70	11	layer	layer	NOUN
fcis-9856	70	12	and	and	CCONJ
fcis-9856	70	13	relu	relu	NOUN
fcis-9856	70	14	activation	activation	NOUN
fcis-9856	70	15	,	,	PUNCT
fcis-9856	70	16	and	and	CCONJ
fcis-9856	70	17	the	the	DET
fcis-9856	70	18	batch	batch	NOUN
fcis-9856	70	19	normalization	normalization	NOUN
fcis-9856	70	20	layer	layer	NOUN
fcis-9856	70	21	is	be	AUX
fcis-9856	70	22	followed	follow	VERB
fcis-9856	70	23	by	by	ADP
fcis-9856	70	24	a	a	DET
fcis-9856	70	25	second	second	ADJ
fcis-9856	70	26	convolutional	convolutional	ADJ
fcis-9856	70	27	layer	layer	NOUN
fcis-9856	70	28	,	,	PUNCT
fcis-9856	70	29	where	where	SCONJ
fcis-9856	70	30	the	the	DET
fcis-9856	70	31	features	feature	NOUN
fcis-9856	70	32	of	of	ADP
fcis-9856	70	33	each	each	DET
fcis-9856	70	34	block	block	NOUN
fcis-9856	70	35	are	be	AUX
fcis-9856	70	36	added	add	VERB
fcis-9856	70	37	one	one	NUM
fcis-9856	70	38	by	by	ADP
fcis-9856	70	39	one	one	NUM
fcis-9856	70	40	with	with	ADP
fcis-9856	70	41	the	the	DET
fcis-9856	70	42	features	feature	NOUN
fcis-9856	70	43	of	of	ADP
fcis-9856	70	44	the	the	DET
fcis-9856	70	45	previous	previous	ADJ
fcis-9856	70	46	block	block	NOUN
fcis-9856	70	47	.	.	PUNCT
fcis-9856	71	1	in	in	ADP
fcis-9856	71	2	the	the	DET
fcis-9856	71	3	last	last	ADJ
fcis-9856	71	4	layer	layer	NOUN
fcis-9856	71	5	of	of	ADP
fcis-9856	71	6	the	the	DET
fcis-9856	71	7	resnet34	resnet34	NOUN
fcis-9856	71	8	architecture	architecture	NOUN
fcis-9856	71	9	,	,	PUNCT
fcis-9856	71	10	the	the	DET
fcis-9856	71	11	size	size	NOUN
fcis-9856	71	12	of	of	ADP
fcis-9856	71	13	the	the	DET
fcis-9856	71	14	kernel	kernel	NOUN
fcis-9856	71	15	is	be	AUX
fcis-9856	71	16	set	set	VERB
fcis-9856	71	17	to	to	ADP
fcis-9856	71	18	3	3	NUM
fcis-9856	71	19	to	to	PART
fcis-9856	71	20	overcome	overcome	VERB
fcis-9856	71	21	the	the	DET
fcis-9856	71	22	blurring	blur	VERB
fcis-9856	71	23	effect	effect	NOUN
fcis-9856	71	24	and	and	CCONJ
fcis-9856	71	25	to	to	PART
fcis-9856	71	26	retain	retain	VERB
fcis-9856	71	27	more	more	ADJ
fcis-9856	71	28	features	feature	NOUN
fcis-9856	71	29	.	.	PUNCT
fcis-9856	72	1	the	the	DET
fcis-9856	72	2	last	last	ADJ
fcis-9856	72	3	layer	layer	NOUN
fcis-9856	72	4	is	be	AUX
fcis-9856	72	5	added	add	VERB
fcis-9856	72	6	with	with	ADP
fcis-9856	72	7	the	the	DET
fcis-9856	72	8	feature	feature	NOUN
fcis-9856	72	9	maps	map	NOUN
fcis-9856	72	10	of	of	ADP
fcis-9856	72	11	the	the	DET
fcis-9856	72	12	first	first	ADJ
fcis-9856	72	13	part	part	NOUN
fcis-9856	72	14	as	as	ADP
fcis-9856	72	15	elements	element	NOUN
fcis-9856	72	16	.	.	PUNCT
fcis-9856	73	1	after	after	ADP
fcis-9856	73	2	the	the	DET
fcis-9856	73	3	depth	depth	NOUN
fcis-9856	73	4	feature	feature	NOUN
fcis-9856	73	5	extraction	extraction	NOUN
fcis-9856	73	6	,	,	PUNCT
fcis-9856	73	7	the	the	DET
fcis-9856	73	8	extracted	extract	VERB
fcis-9856	73	9	features	feature	NOUN
fcis-9856	73	10	are	be	AUX
fcis-9856	73	11	boosted	boost	VERB
fcis-9856	73	12	by	by	ADP
fcis-9856	73	13	using	use	VERB
fcis-9856	73	14	a	a	DET
fcis-9856	73	15	sub	sub	ADJ
fcis-9856	73	16	-	-	ADJ
fcis-9856	73	17	pixel	pixel	ADJ
fcis-9856	73	18	convolutional	convolutional	ADJ
fcis-9856	73	19	layer	layer	NOUN
fcis-9856	73	20	,	,	PUNCT
fcis-9856	73	21	and	and	CCONJ
fcis-9856	73	22	after	after	ADP
fcis-9856	73	23	obtaining	obtain	VERB
fcis-9856	73	24	the	the	DET
fcis-9856	73	25	2	2	NUM
fcis-9856	73	26	-	-	ADJ
fcis-9856	73	27	fold	fold	ADJ
fcis-9856	73	28	zoomed	zoomed	ADJ
fcis-9856	73	29	lr	lr	PROPN
fcis-9856	73	30	image	image	NOUN
fcis-9856	73	31	features	feature	NOUN
fcis-9856	73	32	,	,	PUNCT
fcis-9856	73	33	they	they	PRON
fcis-9856	73	34	are	be	AUX
fcis-9856	73	35	used	use	VERB
fcis-9856	73	36	as	as	ADP
fcis-9856	73	37	the	the	DET
fcis-9856	73	38	input	input	NOUN
fcis-9856	73	39	of	of	ADP
fcis-9856	73	40	this	this	DET
fcis-9856	73	41	architecture	architecture	NOUN
fcis-9856	73	42	in	in	ADP
fcis-9856	73	43	step	step	NOUN
fcis-9856	73	44	3	3	NUM
fcis-9856	73	45	.	.	NOUN
fcis-9856	74	1	2.2.4	2.2.4	NUM
fcis-9856	74	2	.	.	PUNCT
fcis-9856	74	3	enhanced	enhance	VERB
fcis-9856	74	4	feature	feature	NOUN
fcis-9856	74	5	extraction	extraction	NOUN
fcis-9856	74	6	and	and	CCONJ
fcis-9856	74	7	2x	2x	NUM
fcis-9856	74	8	upgrade	upgrade	VERB
fcis-9856	74	9	enhancement	enhancement	NOUN
fcis-9856	74	10	in	in	ADP
fcis-9856	74	11	this	this	DET
fcis-9856	74	12	step	step	NOUN
fcis-9856	74	13	,	,	PUNCT
fcis-9856	74	14	a	a	DET
fcis-9856	74	15	2x	2x	NUM
fcis-9856	74	16	magnified	magnify	VERB
fcis-9856	74	17	lr	lr	NOUN
fcis-9856	74	18	image	image	NOUN
fcis-9856	74	19	is	be	AUX
fcis-9856	74	20	obtained	obtain	VERB
fcis-9856	74	21	,	,	PUNCT
fcis-9856	74	22	after	after	ADP
fcis-9856	74	23	which	which	PRON
fcis-9856	74	24	it	it	PRON
fcis-9856	74	25	is	be	AUX
fcis-9856	74	26	necessary	necessary	ADJ
fcis-9856	74	27	to	to	PART
fcis-9856	74	28	calculate	calculate	VERB
fcis-9856	74	29	the	the	DET
fcis-9856	74	30	features	feature	NOUN
fcis-9856	74	31	of	of	ADP
fcis-9856	74	32	the	the	DET
fcis-9856	74	33	2x	2x	NUM
fcis-9856	74	34	magnified	magnified	ADJ
fcis-9856	74	35	version	version	NOUN
fcis-9856	74	36	.	.	PUNCT
fcis-9856	75	1	first	first	ADV
fcis-9856	75	2	,	,	PUNCT
fcis-9856	75	3	a	a	DET
fcis-9856	75	4	mini	mini	NOUN
fcis-9856	75	5	-	-	NOUN
fcis-9856	75	6	network	network	NOUN
fcis-9856	75	7	including	include	VERB
fcis-9856	75	8	three	three	NUM
fcis-9856	75	9	residual	residual	ADJ
fcis-9856	75	10	blocks	block	NOUN
fcis-9856	75	11	is	be	AUX
fcis-9856	75	12	used	use	VERB
fcis-9856	75	13	,	,	PUNCT
fcis-9856	75	14	as	as	SCONJ
fcis-9856	75	15	shown	show	VERB
fcis-9856	75	16	in	in	ADP
fcis-9856	75	17	figure	figure	NOUN
fcis-9856	75	18	6	6	NUM
fcis-9856	75	19	.	.	PUNCT
fcis-9856	76	1	each	each	DET
fcis-9856	76	2	residual	residual	ADJ
fcis-9856	76	3	block	block	NOUN
fcis-9856	76	4	has	have	VERB
fcis-9856	76	5	two	two	NUM
fcis-9856	76	6	convolutional	convolutional	ADJ
fcis-9856	76	7	layers	layer	NOUN
fcis-9856	76	8	,	,	PUNCT
fcis-9856	76	9	the	the	DET
fcis-9856	76	10	first	first	ADJ
fcis-9856	76	11	convolutional	convolutional	ADJ
fcis-9856	76	12	layer	layer	NOUN
fcis-9856	76	13	is	be	AUX
fcis-9856	76	14	followed	follow	VERB
fcis-9856	76	15	by	by	ADP
fcis-9856	76	16	the	the	DET
fcis-9856	76	17	batch	batch	NOUN
fcis-9856	76	18	normalization	normalization	NOUN
fcis-9856	76	19	layer	layer	NOUN
fcis-9856	76	20	and	and	CCONJ
fcis-9856	76	21	relu	relu	NOUN
fcis-9856	76	22	activation	activation	NOUN
fcis-9856	76	23	,	,	PUNCT
fcis-9856	76	24	and	and	CCONJ
fcis-9856	76	25	the	the	DET
fcis-9856	76	26	batch	batch	NOUN
fcis-9856	76	27	normalization	normalization	NOUN
fcis-9856	76	28	layer	layer	NOUN
fcis-9856	76	29	is	be	AUX
fcis-9856	76	30	followed	follow	VERB
fcis-9856	76	31	by	by	ADP
fcis-9856	76	32	the	the	DET
fcis-9856	76	33	second	second	ADJ
fcis-9856	76	34	convolutional	convolutional	ADJ
fcis-9856	76	35	layer	layer	NOUN
fcis-9856	76	36	.	.	PUNCT
fcis-9856	77	1	at	at	ADP
fcis-9856	77	2	the	the	DET
fcis-9856	77	3	end	end	NOUN
fcis-9856	77	4	of	of	ADP
fcis-9856	77	5	the	the	DET
fcis-9856	77	6	mini	mini	ADJ
fcis-9856	77	7	-	-	ADJ
fcis-9856	77	8	residual	residual	ADJ
fcis-9856	77	9	network	network	NOUN
fcis-9856	77	10	,	,	PUNCT
fcis-9856	77	11	the	the	DET
fcis-9856	77	12	batch	batch	NOUN
fcis-9856	77	13	normalization	normalization	NOUN
fcis-9856	77	14	layer	layer	NOUN
fcis-9856	77	15	is	be	AUX
fcis-9856	77	16	used	use	VERB
fcis-9856	77	17	with	with	ADP
fcis-9856	77	18	a	a	DET
fcis-9856	77	19	192channel	192channel	PROPN
fcis-9856	77	20	-	-	PUNCT
fcis-9856	77	21	wide	wide	ADJ
fcis-9856	77	22	convolutional	convolutional	ADJ
fcis-9856	77	23	layer	layer	NOUN
fcis-9856	77	24	.	.	PUNCT
fcis-9856	78	1	in	in	ADP
fcis-9856	78	2	a	a	DET
fcis-9856	78	3	subsequent	subsequent	ADJ
fcis-9856	78	4	step	step	NOUN
fcis-9856	78	5	,	,	PUNCT
fcis-9856	78	6	the	the	DET
fcis-9856	78	7	feature	feature	NOUN
fcis-9856	78	8	map	map	NOUN
fcis-9856	78	9	obtained	obtain	VERB
fcis-9856	78	10	after	after	ADP
fcis-9856	78	11	the	the	DET
fcis-9856	78	12	2	2	NUM
fcis-9856	78	13	-	-	ADJ
fcis-9856	78	14	fold	fold	ADJ
fcis-9856	78	15	zoom	zoom	NOUN
fcis-9856	78	16	is	be	AUX
fcis-9856	78	17	summed	sum	VERB
fcis-9856	78	18	with	with	ADP
fcis-9856	78	19	the	the	DET
fcis-9856	78	20	feature	feature	NOUN
fcis-9856	78	21	map	map	NOUN
fcis-9856	78	22	elements	element	NOUN
fcis-9856	78	23	extracted	extract	VERB
fcis-9856	78	24	using	use	VERB
fcis-9856	78	25	the	the	DET
fcis-9856	78	26	mini	mini	ADJ
fcis-9856	78	27	network	network	NOUN
fcis-9856	78	28	,	,	PUNCT
fcis-9856	78	29	and	and	CCONJ
fcis-9856	78	30	this	this	DET
fcis-9856	78	31	feature	feature	NOUN
fcis-9856	78	32	map	map	NOUN
fcis-9856	78	33	is	be	AUX
fcis-9856	78	34	then	then	ADV
fcis-9856	78	35	zoomed	zoom	VERB
fcis-9856	78	36	by	by	ADP
fcis-9856	78	37	a	a	DET
fcis-9856	78	38	factor	factor	NOUN
fcis-9856	78	39	of	of	ADP
fcis-9856	78	40	2	2	NUM
fcis-9856	78	41	using	use	VERB
fcis-9856	78	42	a	a	DET
fcis-9856	78	43	sub	sub	ADJ
fcis-9856	78	44	-	-	ADJ
fcis-9856	78	45	pixel	pixel	ADJ
fcis-9856	78	46	convolutional	convolutional	ADJ
fcis-9856	78	47	layer	layer	NOUN
fcis-9856	78	48	.	.	PUNCT
fcis-9856	79	1	after	after	ADP
fcis-9856	79	2	obtaining	obtain	VERB
fcis-9856	79	3	the	the	DET
fcis-9856	79	4	4x	4x	NUM
fcis-9856	79	5	magnified	magnify	VERB
fcis-9856	79	6	feature	feature	NOUN
fcis-9856	79	7	map	map	NOUN
fcis-9856	79	8	of	of	ADP
fcis-9856	79	9	the	the	DET
fcis-9856	79	10	lr	lr	NOUN
fcis-9856	79	11	image	image	NOUN
fcis-9856	79	12	,	,	PUNCT
fcis-9856	79	13	the	the	DET
fcis-9856	79	14	4x	4x	NUM
fcis-9856	79	15	magnified	magnify	VERB
fcis-9856	79	16	features	feature	NOUN
fcis-9856	79	17	are	be	AUX
fcis-9856	79	18	mapped	map	VERB
fcis-9856	79	19	back	back	ADV
fcis-9856	79	20	to	to	ADP
fcis-9856	79	21	the	the	DET
fcis-9856	79	22	hr	hr	NOUN
fcis-9856	79	23	image	image	NOUN
fcis-9856	79	24	using	use	VERB
fcis-9856	79	25	a	a	DET
fcis-9856	79	26	convolutional	convolutional	ADJ
fcis-9856	79	27	reconstruction	reconstruction	NOUN
fcis-9856	79	28	layer	layer	NOUN
fcis-9856	79	29	.	.	PUNCT
fcis-9856	80	1	2.2.5	2.2.5	X
fcis-9856	80	2	.	.	PUNCT
fcis-9856	80	3	discriminator	discriminator	NOUN
fcis-9856	80	4	network	network	NOUN
fcis-9856	80	5	since	since	SCONJ
fcis-9856	80	6	the	the	DET
fcis-9856	80	7	generator	generator	NOUN
fcis-9856	80	8	model	model	NOUN
fcis-9856	80	9	in	in	ADP
fcis-9856	80	10	this	this	DET
fcis-9856	80	11	paper	paper	NOUN
fcis-9856	80	12	is	be	AUX
fcis-9856	80	13	a	a	DET
fcis-9856	80	14	deep	deep	ADJ
fcis-9856	80	15	network	network	NOUN
fcis-9856	80	16	,	,	PUNCT
fcis-9856	80	17	important	important	ADJ
fcis-9856	80	18	modifications	modification	NOUN
fcis-9856	80	19	were	be	AUX
fcis-9856	80	20	made	make	VERB
fcis-9856	80	21	to	to	ADP
fcis-9856	80	22	the	the	DET
fcis-9856	80	23	traditional	traditional	ADJ
fcis-9856	80	24	discriminator	discriminator	NOUN
fcis-9856	80	25	srgan	srgan	NOUN
fcis-9856	80	26	.	.	PUNCT
fcis-9856	81	1	first	first	ADV
fcis-9856	81	2	,	,	PUNCT
fcis-9856	81	3	the	the	DET
fcis-9856	81	4	number	number	NOUN
fcis-9856	81	5	of	of	ADP
fcis-9856	81	6	kernels	kernel	NOUN
fcis-9856	81	7	in	in	ADP
fcis-9856	81	8	each	each	DET
fcis-9856	81	9	layer	layer	NOUN
fcis-9856	81	10	is	be	AUX
fcis-9856	81	11	increased	increase	VERB
fcis-9856	81	12	to	to	ADP
fcis-9856	81	13	2048	2048	NUM
fcis-9856	81	14	,	,	PUNCT
fcis-9856	81	15	and	and	CCONJ
fcis-9856	81	16	then	then	ADV
fcis-9856	81	17	the	the	DET
fcis-9856	81	18	number	number	NOUN
fcis-9856	81	19	of	of	ADP
fcis-9856	81	20	kernels	kernel	NOUN
fcis-9856	81	21	is	be	AUX
fcis-9856	81	22	gradually	gradually	ADV
fcis-9856	81	23	reduced	reduce	VERB
fcis-9856	81	24	to	to	ADP
fcis-9856	81	25	512	512	NUM
fcis-9856	81	26	,	,	PUNCT
fcis-9856	81	27	thus	thus	ADV
fcis-9856	81	28	adding	add	VERB
fcis-9856	81	29	more	more	ADV
fcis-9856	81	30	convolutional	convolutional	ADJ
fcis-9856	81	31	layers	layer	NOUN
fcis-9856	81	32	to	to	ADP
fcis-9856	81	33	the	the	DET
fcis-9856	81	34	discriminator	discriminator	NOUN
fcis-9856	81	35	.	.	PUNCT
fcis-9856	82	1	then	then	ADV
fcis-9856	82	2	,	,	PUNCT
fcis-9856	82	3	three	three	NUM
fcis-9856	82	4	additional	additional	ADJ
fcis-9856	82	5	layers	layer	NOUN
fcis-9856	82	6	with	with	ADP
fcis-9856	82	7	the	the	DET
fcis-9856	82	8	number	number	NOUN
fcis-9856	82	9	of	of	ADP
fcis-9856	82	10	kernels	kernel	NOUN
fcis-9856	82	11	equal	equal	ADJ
fcis-9856	82	12	to	to	ADP
fcis-9856	82	13	128	128	NUM
fcis-9856	82	14	,	,	PUNCT
fcis-9856	82	15	256	256	NUM
fcis-9856	82	16	and	and	CCONJ
fcis-9856	82	17	512	512	NUM
fcis-9856	82	18	were	be	AUX
fcis-9856	82	19	added	add	VERB
fcis-9856	82	20	.	.	PUNCT
fcis-9856	83	1	after	after	ADP
fcis-9856	83	2	that	that	SCONJ
fcis-9856	83	3	the	the	DET
fcis-9856	83	4	outputs	output	NOUN
fcis-9856	83	5	were	be	AUX
fcis-9856	83	6	added	add	VERB
fcis-9856	83	7	before	before	ADP
fcis-9856	83	8	these	these	DET
fcis-9856	83	9	three	three	NUM
fcis-9856	83	10	layers	layer	NOUN
fcis-9856	83	11	,	,	PUNCT
fcis-9856	83	12	with	with	ADP
fcis-9856	83	13	the	the	DET
fcis-9856	83	14	final	final	ADJ
fcis-9856	83	15	512	512	NUM
fcis-9856	83	16	kernel	kernel	NOUN
fcis-9856	83	17	layer	layer	NOUN
fcis-9856	83	18	outputs	output	NOUN
fcis-9856	83	19	.	.	PUNCT
fcis-9856	84	1	3	3	X
fcis-9856	84	2	.	.	X
fcis-9856	84	3	experiment	experiment	NOUN
fcis-9856	84	4	due	due	ADP
fcis-9856	84	5	to	to	ADP
fcis-9856	84	6	the	the	DET
fcis-9856	84	7	lack	lack	NOUN
fcis-9856	84	8	of	of	ADP
fcis-9856	84	9	datasets	dataset	NOUN
fcis-9856	84	10	of	of	ADP
fcis-9856	84	11	real	real	ADJ
fcis-9856	84	12	image	image	NOUN
fcis-9856	84	13	pairs	pair	NOUN
fcis-9856	84	14	,	,	PUNCT
fcis-9856	84	15	most	most	ADJ
fcis-9856	84	16	of	of	ADP
fcis-9856	84	17	the	the	DET
fcis-9856	84	18	current	current	ADJ
fcis-9856	84	19	methods	method	NOUN
fcis-9856	84	20	are	be	AUX
fcis-9856	84	21	trained	train	VERB
fcis-9856	84	22	using	use	VERB
fcis-9856	84	23	synthetic	synthetic	ADJ
fcis-9856	84	24	low	low	ADJ
fcis-9856	84	25	-	-	PUNCT
fcis-9856	84	26	resolution	resolution	NOUN
fcis-9856	84	27	images	image	NOUN
fcis-9856	84	28	and	and	CCONJ
fcis-9856	84	29	then	then	ADV
fcis-9856	84	30	the	the	DET
fcis-9856	84	31	trained	train	VERB
fcis-9856	84	32	models	model	NOUN
fcis-9856	84	33	are	be	AUX
fcis-9856	84	34	used	use	VERB
fcis-9856	84	35	to	to	PART
fcis-9856	84	36	validate	validate	VERB
fcis-9856	84	37	the	the	DET
fcis-9856	84	38	super	super	ADJ
fcis-9856	84	39	-	-	ADJ
fcis-9856	84	40	resolution	resolution	ADJ
fcis-9856	84	41	reconstruction	reconstruction	NOUN
fcis-9856	84	42	effect	effect	NOUN
fcis-9856	84	43	on	on	ADP
fcis-9856	84	44	the	the	DET
fcis-9856	84	45	synthetic	synthetic	ADJ
fcis-9856	84	46	lowresolution	lowresolution	NOUN
fcis-9856	84	47	images	image	NOUN
fcis-9856	84	48	,	,	PUNCT
fcis-9856	84	49	but	but	CCONJ
fcis-9856	84	50	both	both	PRON
fcis-9856	84	51	only	only	ADV
fcis-9856	84	52	validate	validate	VERB
fcis-9856	84	53	the	the	DET
fcis-9856	84	54	final	final	ADJ
fcis-9856	84	55	effect	effect	NOUN
fcis-9856	84	56	on	on	ADP
fcis-9856	84	57	very	very	ADV
fcis-9856	84	58	few	few	ADJ
fcis-9856	84	59	real	real	ADJ
fcis-9856	84	60	low	low	ADJ
fcis-9856	84	61	-	-	PUNCT
fcis-9856	84	62	resolution	resolution	NOUN
fcis-9856	84	63	,	,	PUNCT
fcis-9856	84	64	such	such	ADJ
fcis-9856	84	65	as	as	ADP
fcis-9856	84	66	kernelgan	kernelgan	NOUN
fcis-9856	84	67	[	[	X
fcis-9856	84	68	9	9	NUM
fcis-9856	84	69	]	]	PUNCT
fcis-9856	84	70	and	and	CCONJ
fcis-9856	84	71	ikc	ikc	VERB
fcis-9856	85	1	[	[	X
fcis-9856	85	2	10	10	NUM
fcis-9856	85	3	]	]	PUNCT
fcis-9856	85	4	both	both	PRON
fcis-9856	85	5	validate	validate	VERB
fcis-9856	85	6	the	the	DET
fcis-9856	85	7	results	result	NOUN
fcis-9856	85	8	on	on	ADP
fcis-9856	85	9	only	only	ADV
fcis-9856	85	10	two	two	NUM
fcis-9856	85	11	real	real	ADJ
fcis-9856	85	12	lrs	lrs	NOUN
fcis-9856	85	13	.	.	PUNCT
fcis-9856	86	1	in	in	ADP
fcis-9856	86	2	the	the	DET
fcis-9856	86	3	field	field	NOUN
fcis-9856	86	4	of	of	ADP
fcis-9856	86	5	super	super	ADJ
fcis-9856	86	6	-	-	ADJ
fcis-9856	86	7	resolution	resolution	ADJ
fcis-9856	86	8	reconstruction	reconstruction	NOUN
fcis-9856	86	9	of	of	ADP
fcis-9856	86	10	remote	remote	ADJ
fcis-9856	86	11	sensing	sensing	NOUN
fcis-9856	86	12	images	image	NOUN
fcis-9856	86	13	,	,	PUNCT
fcis-9856	86	14	zhang	zhang	PROPN
fcis-9856	87	1	[	[	X
fcis-9856	87	2	11	11	NUM
fcis-9856	87	3	]	]	PUNCT
fcis-9856	87	4	et	et	PROPN
fcis-9856	87	5	al	al	PROPN
fcis-9856	87	6	.	.	PROPN
fcis-9856	87	7	validated	validate	VERB
fcis-9856	87	8	on	on	ADP
fcis-9856	87	9	two	two	NUM
fcis-9856	87	10	jilin-1	jilin-1	PROPN
fcis-9856	87	11	images	image	NOUN
fcis-9856	87	12	and	and	CCONJ
fcis-9856	87	13	zhu	zhu	X
fcis-9856	88	1	[	[	X
fcis-9856	88	2	12	12	NUM
fcis-9856	88	3	]	]	PUNCT
fcis-9856	88	4	et	et	PROPN
fcis-9856	88	5	al	al	PROPN
fcis-9856	88	6	.	.	PROPN
fcis-9856	88	7	validated	validate	VERB
fcis-9856	88	8	on	on	ADP
fcis-9856	88	9	364	364	NUM
fcis-9856	88	10	geoeye-1	geoeye-1	NOUN
fcis-9856	88	11	images	image	NOUN
fcis-9856	88	12	.	.	PUNCT
fcis-9856	89	1	since	since	SCONJ
fcis-9856	89	2	these	these	DET
fcis-9856	89	3	real	real	ADJ
fcis-9856	89	4	low	low	ADJ
fcis-9856	89	5	-	-	PUNCT
fcis-9856	89	6	resolution	resolution	NOUN
fcis-9856	89	7	images	image	NOUN
fcis-9856	89	8	do	do	AUX
fcis-9856	89	9	not	not	PART
fcis-9856	89	10	have	have	VERB
fcis-9856	89	11	corresponding	correspond	VERB
fcis-9856	89	12	highresolution	highresolution	NOUN
fcis-9856	89	13	images	image	NOUN
fcis-9856	89	14	for	for	ADP
fcis-9856	89	15	comparative	comparative	ADJ
fcis-9856	89	16	validation	validation	NOUN
fcis-9856	89	17	,	,	PUNCT
fcis-9856	89	18	only	only	ADV
fcis-9856	89	19	subjective	subjective	ADJ
fcis-9856	89	20	visual	visual	ADJ
fcis-9856	89	21	effects	effect	NOUN
fcis-9856	89	22	are	be	AUX
fcis-9856	89	23	used	use	VERB
fcis-9856	89	24	to	to	PART
fcis-9856	89	25	evaluate	evaluate	VERB
fcis-9856	89	26	the	the	DET
fcis-9856	89	27	effect	effect	NOUN
fcis-9856	89	28	on	on	ADP
fcis-9856	89	29	real	real	ADJ
fcis-9856	89	30	lowresolution	lowresolution	NOUN
fcis-9856	89	31	remote	remote	ADJ
fcis-9856	89	32	sensing	sensing	NOUN
fcis-9856	89	33	images	image	NOUN
fcis-9856	89	34	.	.	PUNCT
fcis-9856	90	1	in	in	ADP
fcis-9856	90	2	this	this	DET
fcis-9856	90	3	paper	paper	NOUN
fcis-9856	90	4	,	,	PUNCT
fcis-9856	90	5	training	training	NOUN
fcis-9856	90	6	on	on	ADP
fcis-9856	90	7	the	the	DET
fcis-9856	90	8	uc	uc	PROPN
fcis-9856	90	9	merced	merced	PROPN
fcis-9856	90	10	dataset	dataset	PROPN
fcis-9856	90	11	and	and	CCONJ
fcis-9856	90	12	super	super	ADJ
fcis-9856	90	13	-	-	ADJ
fcis-9856	90	14	resolution	resolution	ADJ
fcis-9856	90	15	reconstruction	reconstruction	NOUN
fcis-9856	90	16	on	on	ADP
fcis-9856	90	17	the	the	DET
fcis-9856	90	18	real	real	ADJ
fcis-9856	90	19	dataset	dataset	NOUN
fcis-9856	90	20	alsat2b	alsat2b	PROPN
fcis-9856	90	21	with	with	ADP
fcis-9856	90	22	paired	pair	VERB
fcis-9856	90	23	images	image	NOUN
fcis-9856	90	24	can	can	AUX
fcis-9856	90	25	be	be	AUX
fcis-9856	90	26	used	use	VERB
fcis-9856	90	27	to	to	ADP
fcis-9856	90	28	34	34	NUM
fcis-9856	90	29	judge	judge	VERB
fcis-9856	90	30	the	the	DET
fcis-9856	90	31	reconstruction	reconstruction	NOUN
fcis-9856	90	32	effect	effect	NOUN
fcis-9856	90	33	by	by	ADP
fcis-9856	90	34	subjective	subjective	ADJ
fcis-9856	90	35	visual	visual	ADJ
fcis-9856	90	36	effect	effect	NOUN
fcis-9856	90	37	evaluation	evaluation	NOUN
fcis-9856	90	38	and	and	CCONJ
fcis-9856	90	39	objective	objective	ADJ
fcis-9856	90	40	index	index	NOUN
fcis-9856	90	41	.	.	PUNCT
fcis-9856	91	1	compared	compare	VERB
fcis-9856	91	2	with	with	ADP
fcis-9856	91	3	the	the	DET
fcis-9856	91	4	original	original	ADJ
fcis-9856	91	5	method	method	NOUN
fcis-9856	91	6	,	,	PUNCT
fcis-9856	91	7	the	the	DET
fcis-9856	91	8	method	method	NOUN
fcis-9856	91	9	in	in	ADP
fcis-9856	91	10	this	this	DET
fcis-9856	91	11	paper	paper	NOUN
fcis-9856	91	12	qualitatively	qualitatively	ADV
fcis-9856	91	13	and	and	CCONJ
fcis-9856	91	14	quantitatively	quantitatively	ADV
fcis-9856	91	15	validates	validate	VERB
fcis-9856	91	16	the	the	DET
fcis-9856	91	17	effectiveness	effectiveness	NOUN
fcis-9856	91	18	of	of	ADP
fcis-9856	91	19	the	the	DET
fcis-9856	91	20	superresolution	superresolution	NOUN
fcis-9856	91	21	reconstruction	reconstruction	NOUN
fcis-9856	91	22	algorithm	algorithm	NOUN
fcis-9856	91	23	on	on	ADP
fcis-9856	91	24	a	a	DET
fcis-9856	91	25	larger	large	ADJ
fcis-9856	91	26	dataset	dataset	NOUN
fcis-9856	91	27	.	.	PUNCT
fcis-9856	92	1	3.1	3.1	NUM
fcis-9856	92	2	.	.	PUNCT
fcis-9856	92	3	shallow	shallow	ADJ
fcis-9856	92	4	feature	feature	NOUN
fcis-9856	92	5	extraction	extraction	NOUN
fcis-9856	92	6	the	the	DET
fcis-9856	92	7	uc	uc	PROPN
fcis-9856	92	8	merced	merced	PROPN
fcis-9856	92	9	dataset	dataset	PROPN
fcis-9856	92	10	is	be	AUX
fcis-9856	92	11	a	a	DET
fcis-9856	92	12	widely	widely	ADV
fcis-9856	92	13	used	use	VERB
fcis-9856	92	14	dataset	dataset	NOUN
fcis-9856	92	15	in	in	ADP
fcis-9856	92	16	the	the	DET
fcis-9856	92	17	field	field	NOUN
fcis-9856	92	18	of	of	ADP
fcis-9856	92	19	remote	remote	ADJ
fcis-9856	92	20	sensing	sensing	NOUN
fcis-9856	92	21	images	image	NOUN
fcis-9856	92	22	,	,	PUNCT
fcis-9856	92	23	which	which	PRON
fcis-9856	92	24	contains	contain	VERB
fcis-9856	92	25	21	21	NUM
fcis-9856	92	26	types	type	NOUN
fcis-9856	92	27	of	of	ADP
fcis-9856	92	28	landforms	landform	NOUN
fcis-9856	92	29	such	such	ADJ
fcis-9856	92	30	as	as	ADP
fcis-9856	92	31	farmland	farmland	NOUN
fcis-9856	92	32	,	,	PUNCT
fcis-9856	92	33	airports	airport	NOUN
fcis-9856	92	34	,	,	PUNCT
fcis-9856	92	35	beaches	beach	NOUN
fcis-9856	92	36	,	,	PUNCT
fcis-9856	92	37	harbors	harbor	NOUN
fcis-9856	92	38	,	,	PUNCT
fcis-9856	92	39	etc	etc	X
fcis-9856	92	40	.	.	X
fcis-9856	92	41	,	,	PUNCT
fcis-9856	92	42	100	100	NUM
fcis-9856	92	43	images	image	NOUN
fcis-9856	92	44	of	of	ADP
fcis-9856	92	45	each	each	DET
fcis-9856	92	46	type	type	NOUN
fcis-9856	92	47	of	of	ADP
fcis-9856	92	48	landforms	landform	NOUN
fcis-9856	92	49	,	,	PUNCT
fcis-9856	92	50	a	a	DET
fcis-9856	92	51	total	total	NOUN
fcis-9856	92	52	of	of	ADP
fcis-9856	92	53	2100	2100	NUM
fcis-9856	92	54	images	image	NOUN
fcis-9856	92	55	,	,	PUNCT
fcis-9856	92	56	which	which	PRON
fcis-9856	92	57	are	be	AUX
fcis-9856	92	58	randomly	randomly	ADV
fcis-9856	92	59	divided	divide	VERB
fcis-9856	92	60	in	in	ADP
fcis-9856	92	61	the	the	DET
fcis-9856	92	62	text	text	NOUN
fcis-9856	92	63	according	accord	VERB
fcis-9856	92	64	to	to	ADP
fcis-9856	92	65	70	70	NUM
fcis-9856	92	66	%	%	NOUN
fcis-9856	92	67	as	as	ADP
fcis-9856	92	68	training	training	NOUN
fcis-9856	92	69	set	set	NOUN
fcis-9856	92	70	,	,	PUNCT
fcis-9856	92	71	20	20	NUM
fcis-9856	92	72	%	%	NOUN
fcis-9856	92	73	as	as	ADP
fcis-9856	92	74	validation	validation	NOUN
fcis-9856	92	75	set	set	NOUN
fcis-9856	92	76	,	,	PUNCT
fcis-9856	92	77	and	and	CCONJ
fcis-9856	92	78	10	10	NUM
fcis-9856	92	79	%	%	NOUN
fcis-9856	92	80	as	as	SCONJ
fcis-9856	92	81	test	test	NOUN
fcis-9856	92	82	set	set	VERB
fcis-9856	92	83	.	.	PUNCT
fcis-9856	93	1	a1sat2b	a1sat2b	PROPN
fcis-9856	93	2	dataset	dataset	NOUN
fcis-9856	93	3	is	be	AUX
fcis-9856	93	4	used	use	VERB
fcis-9856	93	5	by	by	ADP
fcis-9856	93	6	achraf	achraf	NOUN
fcis-9856	93	7	equal	equal	ADJ
fcis-9856	93	8	to	to	ADP
fcis-9856	93	9	2021	2021	NUM
fcis-9856	93	10	the	the	DET
fcis-9856	93	11	latest	late	ADJ
fcis-9856	93	12	real	real	ADJ
fcis-9856	93	13	remote	remote	ADJ
fcis-9856	93	14	sensing	sense	VERB
fcis-9856	93	15	satellite	satellite	NOUN
fcis-9856	93	16	dataset	dataset	NOUN
fcis-9856	93	17	released	release	VERB
fcis-9856	93	18	in	in	ADP
fcis-9856	93	19	2021	2021	NUM
fcis-9856	93	20	.	.	PUNCT
fcis-9856	94	1	the	the	DET
fcis-9856	94	2	dataset	dataset	NOUN
fcis-9856	94	3	covers	cover	VERB
fcis-9856	94	4	different	different	ADJ
fcis-9856	94	5	vegetation	vegetation	NOUN
fcis-9856	94	6	types	type	NOUN
fcis-9856	94	7	and	and	CCONJ
fcis-9856	94	8	is	be	AUX
fcis-9856	94	9	size	size	NOUN
fcis-9856	94	10	-	-	PUNCT
fcis-9856	94	11	sliced	slice	VERB
fcis-9856	94	12	in	in	ADP
fcis-9856	94	13	blocks	block	NOUN
fcis-9856	94	14	of	of	ADP
fcis-9856	94	15	256	256	NUM
fcis-9856	94	16	*	*	SYM
fcis-9856	94	17	256	256	NUM
fcis-9856	94	18	for	for	ADP
fcis-9856	94	19	high	high	ADJ
fcis-9856	94	20	-	-	PUNCT
fcis-9856	94	21	resolution	resolution	NOUN
fcis-9856	94	22	images	image	NOUN
fcis-9856	94	23	and	and	CCONJ
fcis-9856	94	24	64	64	NUM
fcis-9856	94	25	*	*	NUM
fcis-9856	94	26	64	64	NUM
fcis-9856	94	27	for	for	ADP
fcis-9856	94	28	low	low	ADJ
fcis-9856	94	29	-	-	PUNCT
fcis-9856	94	30	resolution	resolution	NOUN
fcis-9856	94	31	images	image	NOUN
fcis-9856	94	32	to	to	PART
fcis-9856	94	33	obtain	obtain	VERB
fcis-9856	94	34	a	a	DET
fcis-9856	94	35	total	total	NOUN
fcis-9856	94	36	of	of	ADP
fcis-9856	94	37	2,759	2,759	NUM
fcis-9856	94	38	pairs	pair	NOUN
fcis-9856	94	39	of	of	ADP
fcis-9856	94	40	real	real	ADJ
fcis-9856	94	41	remote	remote	ADJ
fcis-9856	94	42	sensing	sense	VERB
fcis-9856	94	43	super	super	ADJ
fcis-9856	94	44	-	-	ADJ
fcis-9856	94	45	resolution	resolution	ADJ
fcis-9856	94	46	datasets	dataset	NOUN
fcis-9856	94	47	with	with	ADP
fcis-9856	94	48	a	a	DET
fcis-9856	94	49	magnification	magnification	NOUN
fcis-9856	94	50	of	of	ADP
fcis-9856	94	51	4	4	NUM
fcis-9856	94	52	,	,	PUNCT
fcis-9856	94	53	of	of	ADP
fcis-9856	94	54	which	which	PRON
fcis-9856	94	55	2,182	2,182	NUM
fcis-9856	94	56	pairs	pair	NOUN
fcis-9856	94	57	are	be	AUX
fcis-9856	94	58	used	use	VERB
fcis-9856	94	59	as	as	ADP
fcis-9856	94	60	the	the	DET
fcis-9856	94	61	training	training	NOUN
fcis-9856	94	62	set	set	NOUN
fcis-9856	94	63	and	and	CCONJ
fcis-9856	94	64	577	577	NUM
fcis-9856	94	65	pairs	pair	NOUN
fcis-9856	94	66	are	be	AUX
fcis-9856	94	67	used	use	VERB
fcis-9856	94	68	as	as	ADP
fcis-9856	94	69	the	the	DET
fcis-9856	94	70	test	test	NOUN
fcis-9856	94	71	set	set	NOUN
fcis-9856	94	72	.	.	PUNCT
fcis-9856	95	1	the	the	DET
fcis-9856	95	2	validation	validation	NOUN
fcis-9856	95	3	set	set	NOUN
fcis-9856	95	4	is	be	AUX
fcis-9856	95	5	divided	divide	VERB
fcis-9856	95	6	into	into	ADP
fcis-9856	95	7	3	3	NUM
fcis-9856	95	8	subsets	subset	NOUN
fcis-9856	95	9	according	accord	VERB
fcis-9856	95	10	to	to	ADP
fcis-9856	95	11	the	the	DET
fcis-9856	95	12	types	type	NOUN
fcis-9856	95	13	of	of	ADP
fcis-9856	95	14	features	feature	NOUN
fcis-9856	95	15	,	,	PUNCT
fcis-9856	95	16	specifically	specifically	ADV
fcis-9856	95	17	239	239	NUM
fcis-9856	95	18	pairs	pair	NOUN
fcis-9856	95	19	of	of	ADP
fcis-9856	95	20	cities	city	NOUN
fcis-9856	95	21	,	,	PUNCT
fcis-9856	95	22	56	56	NUM
fcis-9856	95	23	pairs	pair	NOUN
fcis-9856	95	24	of	of	ADP
fcis-9856	95	25	farmlands	farmland	NOUN
fcis-9856	95	26	and	and	CCONJ
fcis-9856	95	27	282	282	NUM
fcis-9856	95	28	pairs	pair	NOUN
fcis-9856	95	29	of	of	ADP
fcis-9856	95	30	special	special	ADJ
fcis-9856	95	31	buildings	building	NOUN
fcis-9856	95	32	(	(	PUNCT
fcis-9856	95	33	e.g.	e.g.	ADV
fcis-9856	95	34	,	,	PUNCT
fcis-9856	95	35	bridges	bridge	NOUN
fcis-9856	95	36	,	,	PUNCT
fcis-9856	95	37	stadiums	stadium	NOUN
fcis-9856	95	38	,	,	PUNCT
fcis-9856	95	39	etc	etc	X
fcis-9856	95	40	.	.	X
fcis-9856	95	41	)	)	PUNCT
fcis-9856	95	42	for	for	ADP
fcis-9856	95	43	the	the	DET
fcis-9856	95	44	test	test	NOUN
fcis-9856	95	45	set	set	VERB
fcis-9856	95	46	.	.	PUNCT
fcis-9856	96	1	3.2	3.2	NUM
fcis-9856	96	2	.	.	PUNCT
fcis-9856	96	3	experimental	experimental	ADJ
fcis-9856	96	4	setup	setup	NOUN
fcis-9856	96	5	in	in	ADP
fcis-9856	96	6	order	order	NOUN
fcis-9856	96	7	to	to	PART
fcis-9856	96	8	verify	verify	VERB
fcis-9856	96	9	the	the	DET
fcis-9856	96	10	effectiveness	effectiveness	NOUN
fcis-9856	96	11	of	of	ADP
fcis-9856	96	12	the	the	DET
fcis-9856	96	13	degradation	degradation	NOUN
fcis-9856	96	14	model	model	NOUN
fcis-9856	96	15	and	and	CCONJ
fcis-9856	96	16	reconstruction	reconstruction	NOUN
fcis-9856	96	17	model	model	NOUN
fcis-9856	96	18	in	in	ADP
fcis-9856	96	19	this	this	DET
fcis-9856	96	20	paper	paper	NOUN
fcis-9856	96	21	,	,	PUNCT
fcis-9856	96	22	multiple	multiple	ADJ
fcis-9856	96	23	sets	set	NOUN
fcis-9856	96	24	of	of	ADP
fcis-9856	96	25	experiments	experiment	NOUN
fcis-9856	96	26	are	be	AUX
fcis-9856	96	27	conducted	conduct	VERB
fcis-9856	96	28	on	on	ADP
fcis-9856	96	29	the	the	DET
fcis-9856	96	30	uc	uc	PROPN
fcis-9856	96	31	merced	merced	PROPN
fcis-9856	96	32	dataset	dataset	PROPN
fcis-9856	96	33	and	and	CCONJ
fcis-9856	96	34	the	the	DET
fcis-9856	96	35	alsat2b	alsat2b	PROPN
fcis-9856	96	36	dataset	dataset	NOUN
fcis-9856	96	37	.	.	PUNCT
fcis-9856	97	1	firstly	firstly	ADV
fcis-9856	97	2	,	,	PUNCT
fcis-9856	97	3	the	the	DET
fcis-9856	97	4	degradation	degradation	NOUN
fcis-9856	97	5	model	model	NOUN
fcis-9856	97	6	is	be	AUX
fcis-9856	97	7	used	use	VERB
fcis-9856	97	8	to	to	PART
fcis-9856	97	9	process	process	VERB
fcis-9856	97	10	the	the	DET
fcis-9856	97	11	uc	uc	PROPN
fcis-9856	97	12	merced	merced	PROPN
fcis-9856	97	13	dataset	dataset	VERB
fcis-9856	97	14	to	to	PART
fcis-9856	97	15	obtain	obtain	VERB
fcis-9856	97	16	the	the	DET
fcis-9856	97	17	corresponding	corresponding	ADJ
fcis-9856	97	18	low	low	ADJ
fcis-9856	97	19	-	-	PUNCT
fcis-9856	97	20	resolution	resolution	NOUN
fcis-9856	97	21	images	image	NOUN
fcis-9856	97	22	;	;	PUNCT
fcis-9856	97	23	then	then	ADV
fcis-9856	97	24	the	the	DET
fcis-9856	97	25	image	image	NOUN
fcis-9856	97	26	pairs	pair	NOUN
fcis-9856	97	27	are	be	AUX
fcis-9856	97	28	composed	compose	VERB
fcis-9856	97	29	to	to	PART
fcis-9856	97	30	train	train	VERB
fcis-9856	97	31	the	the	DET
fcis-9856	97	32	super	super	ADJ
fcis-9856	97	33	-	-	ADJ
fcis-9856	97	34	resolution	resolution	ADJ
fcis-9856	97	35	reconstruction	reconstruction	NOUN
fcis-9856	97	36	network	network	NOUN
fcis-9856	97	37	;	;	PUNCT
fcis-9856	97	38	finally	finally	ADV
fcis-9856	97	39	,	,	PUNCT
fcis-9856	97	40	the	the	DET
fcis-9856	97	41	super	super	ADJ
fcis-9856	97	42	-	-	ADJ
fcis-9856	97	43	resolution	resolution	ADJ
fcis-9856	97	44	reconstruction	reconstruction	NOUN
fcis-9856	97	45	effect	effect	NOUN
fcis-9856	97	46	is	be	AUX
fcis-9856	97	47	verified	verify	VERB
fcis-9856	97	48	on	on	ADP
fcis-9856	97	49	the	the	DET
fcis-9856	97	50	uc	uc	PROPN
fcis-9856	97	51	merced	merced	PROPN
fcis-9856	97	52	test	test	PROPN
fcis-9856	97	53	set	set	VERB
fcis-9856	97	54	and	and	CCONJ
fcis-9856	97	55	the	the	DET
fcis-9856	97	56	alsat2b	alsat2b	PROPN
fcis-9856	97	57	test	test	NOUN
fcis-9856	97	58	set	set	NOUN
fcis-9856	97	59	,	,	PUNCT
fcis-9856	97	60	respectively	respectively	ADV
fcis-9856	97	61	,	,	PUNCT
fcis-9856	97	62	with	with	ADP
fcis-9856	97	63	the	the	DET
fcis-9856	97	64	human	human	ADJ
fcis-9856	97	65	subjective	subjective	ADJ
fcis-9856	97	66	visual	visual	ADJ
fcis-9856	97	67	effect	effect	NOUN
fcis-9856	97	68	as	as	ADP
fcis-9856	97	69	the	the	DET
fcis-9856	97	70	qualitative	qualitative	ADJ
fcis-9856	97	71	evaluation	evaluation	NOUN
fcis-9856	97	72	index	index	NOUN
fcis-9856	97	73	,	,	PUNCT
fcis-9856	97	74	and	and	CCONJ
fcis-9856	97	75	the	the	DET
fcis-9856	97	76	peak	peak	NOUN
fcis-9856	97	77	signal	signal	NOUN
fcis-9856	97	78	-	-	PUNCT
fcis-9856	97	79	to	to	ADP
fcis-9856	97	80	-	-	PUNCT
fcis-9856	97	81	noise	noise	NOUN
fcis-9856	97	82	ratio	ratio	NOUN
fcis-9856	97	83	(	(	PUNCT
fcis-9856	97	84	psnr	psnr	NOUN
fcis-9856	97	85	)	)	PUNCT
fcis-9856	97	86	and	and	CCONJ
fcis-9856	97	87	structural	structural	ADJ
fcis-9856	97	88	similarity	similarity	NOUN
fcis-9856	97	89	(	(	PUNCT
fcis-9856	97	90	ssim	ssim	NOUN
fcis-9856	97	91	)	)	PUNCT
fcis-9856	97	92	as	as	ADP
fcis-9856	97	93	objective	objective	ADJ
fcis-9856	97	94	evaluation	evaluation	NOUN
fcis-9856	97	95	indexes	index	NOUN
fcis-9856	97	96	.	.	PUNCT
fcis-9856	98	1	the	the	DET
fcis-9856	98	2	size	size	NOUN
fcis-9856	98	3	of	of	ADP
fcis-9856	98	4	the	the	DET
fcis-9856	98	5	convolution	convolution	NOUN
fcis-9856	98	6	kernel	kernel	NOUN
fcis-9856	98	7	of	of	ADP
fcis-9856	98	8	the	the	DET
fcis-9856	98	9	residual	residual	ADJ
fcis-9856	98	10	module	module	NOUN
fcis-9856	98	11	in	in	ADP
fcis-9856	98	12	the	the	DET
fcis-9856	98	13	reconstruction	reconstruction	NOUN
fcis-9856	98	14	network	network	NOUN
fcis-9856	98	15	is	be	AUX
fcis-9856	98	16	3	3	NUM
fcis-9856	98	17	*	*	SYM
fcis-9856	98	18	3	3	NUM
fcis-9856	98	19	.	.	PUNCT
fcis-9856	99	1	the	the	DET
fcis-9856	99	2	experiments	experiment	NOUN
fcis-9856	99	3	are	be	AUX
fcis-9856	99	4	performed	perform	VERB
fcis-9856	99	5	with	with	ADP
fcis-9856	99	6	adam	adam	PROPN
fcis-9856	99	7	optimizer	optimizer	NOUN
fcis-9856	99	8	,	,	PUNCT
fcis-9856	99	9	1	1	PROPN
fcis-9856	99	10	=	=	SYM
fcis-9856	99	11	0.9	0.9	NUM
fcis-9856	99	12	,	,	PUNCT
fcis-9856	99	13	2	2	NUM
fcis-9856	99	14	=	=	NOUN
fcis-9856	99	15	0.99	0.99	NUM
fcis-9856	99	16	,	,	PUNCT
fcis-9856	99	17			PROPN
fcis-9856	99	18	=	=	NOUN
fcis-9856	99	19	10	10	NUM
fcis-9856	99	20	-	-	SYM
fcis-9856	99	21	8	8	NUM
fcis-9856	99	22	,	,	PUNCT
fcis-9856	99	23	learning	learn	VERB
fcis-9856	99	24	rate	rate	NOUN
fcis-9856	99	25	of	of	ADP
fcis-9856	99	26	0.0001	0.0001	NUM
fcis-9856	99	27	,	,	PUNCT
fcis-9856	99	28	batch	batch	NOUN
fcis-9856	99	29	size	size	NOUN
fcis-9856	99	30	of	of	ADP
fcis-9856	99	31	16	16	NUM
fcis-9856	99	32	,	,	PUNCT
fcis-9856	99	33	and	and	CCONJ
fcis-9856	99	34	20,000	20,000	NUM
fcis-9856	99	35	training	training	NOUN
fcis-9856	99	36	rounds	round	NOUN
fcis-9856	99	37	on	on	ADP
fcis-9856	99	38	a	a	DET
fcis-9856	99	39	single	single	ADJ
fcis-9856	99	40	32	32	NUM
fcis-9856	99	41	g	g	PROPN
fcis-9856	99	42	nvidia	nvidia	PROPN
fcis-9856	99	43	tesla	tesla	PROPN
fcis-9856	99	44	v100	v100	PROPN
fcis-9856	99	45	graphics	graphic	NOUN
fcis-9856	99	46	card	card	NOUN
fcis-9856	99	47	.	.	PUNCT
fcis-9856	100	1	after	after	SCONJ
fcis-9856	100	2	the	the	DET
fcis-9856	100	3	training	training	NOUN
fcis-9856	100	4	rounds	round	NOUN
fcis-9856	100	5	reached	reach	VERB
fcis-9856	100	6	15,000	15,000	NUM
fcis-9856	100	7	,	,	PUNCT
fcis-9856	100	8	it	it	PRON
fcis-9856	100	9	gradually	gradually	ADV
fcis-9856	100	10	converged	converge	VERB
fcis-9856	100	11	.	.	PUNCT
fcis-9856	101	1	3.3	3.3	NUM
fcis-9856	101	2	.	.	PUNCT
fcis-9856	102	1	experimental	experimental	ADJ
fcis-9856	102	2	results	result	NOUN
fcis-9856	102	3	the	the	DET
fcis-9856	102	4	psnr	psnr	NOUN
fcis-9856	102	5	and	and	CCONJ
fcis-9856	102	6	ssim	ssim	NOUN
fcis-9856	102	7	are	be	AUX
fcis-9856	102	8	calculated	calculate	VERB
fcis-9856	102	9	on	on	ADP
fcis-9856	102	10	the	the	DET
fcis-9856	102	11	uc	uc	PROPN
fcis-9856	102	12	merced	merced	PROPN
fcis-9856	102	13	dataset	dataset	PROPN
fcis-9856	102	14	,	,	PUNCT
fcis-9856	102	15	and	and	CCONJ
fcis-9856	102	16	the	the	DET
fcis-9856	102	17	psnr	psnr	NOUN
fcis-9856	102	18	/	/	SYM
fcis-9856	102	19	ssjm	ssjm	NOUN
fcis-9856	102	20	of	of	ADP
fcis-9856	102	21	this	this	DET
fcis-9856	102	22	paper	paper	NOUN
fcis-9856	102	23	improves	improve	VERB
fcis-9856	102	24	1.4071	1.4071	NUM
fcis-9856	102	25	db/0.0672	db/0.0672	X
fcis-9856	102	26	compared	compare	VERB
fcis-9856	102	27	with	with	ADP
fcis-9856	102	28	esrgan	esrgan	NOUN
fcis-9856	102	29	and	and	CCONJ
fcis-9856	102	30	0.8211	0.8211	NUM
fcis-9856	102	31	db/0.0235	db/0.0235	PUNCT
fcis-9856	102	32	compared	compare	VERB
fcis-9856	102	33	with	with	ADP
fcis-9856	102	34	rcan	rcan	ADJ
fcis-9856	102	35	.	.	PUNCT
fcis-9856	103	1	the	the	DET
fcis-9856	103	2	reconstructed	reconstructed	ADJ
fcis-9856	103	3	image	image	NOUN
fcis-9856	103	4	of	of	ADP
fcis-9856	103	5	rcan	rcan	ADJ
fcis-9856	103	6	method	method	NOUN
fcis-9856	103	7	is	be	AUX
fcis-9856	103	8	too	too	ADV
fcis-9856	103	9	smooth	smooth	ADJ
fcis-9856	103	10	and	and	CCONJ
fcis-9856	103	11	the	the	DET
fcis-9856	103	12	texture	texture	ADJ
fcis-9856	103	13	effect	effect	NOUN
fcis-9856	103	14	is	be	AUX
fcis-9856	103	15	not	not	PART
fcis-9856	103	16	good	good	ADJ
fcis-9856	103	17	;	;	PUNCT
fcis-9856	103	18	while	while	SCONJ
fcis-9856	103	19	the	the	DET
fcis-9856	103	20	method	method	NOUN
fcis-9856	103	21	in	in	ADP
fcis-9856	103	22	this	this	DET
fcis-9856	103	23	paper	paper	NOUN
fcis-9856	103	24	enhances	enhance	VERB
fcis-9856	103	25	the	the	DET
fcis-9856	103	26	image	image	NOUN
fcis-9856	103	27	details	detail	NOUN
fcis-9856	103	28	better	well	ADV
fcis-9856	103	29	and	and	CCONJ
fcis-9856	103	30	is	be	AUX
fcis-9856	103	31	visually	visually	ADV
fcis-9856	103	32	better	well	ADJ
fcis-9856	103	33	than	than	ADP
fcis-9856	103	34	other	other	ADJ
fcis-9856	103	35	algorithms	algorithm	NOUN
fcis-9856	103	36	.	.	PUNCT
fcis-9856	104	1	in	in	ADP
fcis-9856	104	2	order	order	NOUN
fcis-9856	104	3	to	to	PART
fcis-9856	104	4	further	far	ADV
fcis-9856	104	5	verify	verify	VERB
fcis-9856	104	6	the	the	DET
fcis-9856	104	7	effectiveness	effectiveness	NOUN
fcis-9856	104	8	of	of	ADP
fcis-9856	104	9	the	the	DET
fcis-9856	104	10	degradation	degradation	NOUN
fcis-9856	104	11	model	model	NOUN
fcis-9856	104	12	,	,	PUNCT
fcis-9856	104	13	this	this	DET
fcis-9856	104	14	paper	paper	NOUN
fcis-9856	104	15	uses	use	VERB
fcis-9856	104	16	three	three	NUM
fcis-9856	104	17	different	different	ADJ
fcis-9856	104	18	training	training	NOUN
fcis-9856	104	19	sets	set	NOUN
fcis-9856	104	20	trained	train	VERB
fcis-9856	104	21	by	by	ADP
fcis-9856	104	22	the	the	DET
fcis-9856	104	23	reconstruction	reconstruction	NOUN
fcis-9856	104	24	model	model	NOUN
fcis-9856	104	25	for	for	ADP
fcis-9856	104	26	comparison	comparison	NOUN
fcis-9856	104	27	experiments	experiment	NOUN
fcis-9856	104	28	.	.	PUNCT
fcis-9856	105	1	one	one	NUM
fcis-9856	105	2	is	be	AUX
fcis-9856	105	3	the	the	DET
fcis-9856	105	4	uc	uc	PROPN
fcis-9856	105	5	merced	merced	PROPN
fcis-9856	105	6	dataset	dataset	PROPN
fcis-9856	105	7	processed	process	VERB
fcis-9856	105	8	with	with	ADP
fcis-9856	105	9	dual	dual	ADJ
fcis-9856	105	10	triple	triple	ADJ
fcis-9856	105	11	interpolation	interpolation	NOUN
fcis-9856	105	12	;	;	PUNCT
fcis-9856	105	13	the	the	DET
fcis-9856	105	14	second	second	NOUN
fcis-9856	105	15	is	be	AUX
fcis-9856	105	16	the	the	DET
fcis-9856	105	17	uc	uc	PROPN
fcis-9856	105	18	merced	merced	PROPN
fcis-9856	105	19	dataset	dataset	PROPN
fcis-9856	105	20	processed	process	VERB
fcis-9856	105	21	with	with	ADP
fcis-9856	105	22	the	the	DET
fcis-9856	105	23	degradation	degradation	NOUN
fcis-9856	105	24	model	model	NOUN
fcis-9856	105	25	in	in	ADP
fcis-9856	105	26	this	this	DET
fcis-9856	105	27	paper	paper	NOUN
fcis-9856	105	28	;	;	PUNCT
fcis-9856	105	29	and	and	CCONJ
fcis-9856	105	30	the	the	DET
fcis-9856	105	31	third	third	NOUN
fcis-9856	105	32	is	be	AUX
fcis-9856	105	33	trained	train	VERB
fcis-9856	105	34	directly	directly	ADV
fcis-9856	105	35	with	with	ADP
fcis-9856	105	36	the	the	DET
fcis-9856	105	37	training	training	NOUN
fcis-9856	105	38	set	set	VERB
fcis-9856	105	39	in	in	ADP
fcis-9856	105	40	alsat2b	alsat2b	PROPN
fcis-9856	105	41	.	.	PUNCT
fcis-9856	106	1	the	the	DET
fcis-9856	106	2	reconstruction	reconstruction	NOUN
fcis-9856	106	3	models	model	NOUN
fcis-9856	106	4	trained	train	VERB
fcis-9856	106	5	on	on	ADP
fcis-9856	106	6	the	the	DET
fcis-9856	106	7	three	three	NUM
fcis-9856	106	8	different	different	ADJ
fcis-9856	106	9	training	training	NOUN
fcis-9856	106	10	sets	set	NOUN
fcis-9856	106	11	are	be	AUX
fcis-9856	106	12	validated	validate	VERB
fcis-9856	106	13	on	on	ADP
fcis-9856	106	14	the	the	DET
fcis-9856	106	15	alsat2b	alsat2b	PROPN
fcis-9856	106	16	validation	validation	NOUN
fcis-9856	106	17	set	set	VERB
fcis-9856	106	18	respectively	respectively	ADV
fcis-9856	106	19	.	.	PUNCT
fcis-9856	107	1	table	table	NOUN
fcis-9856	107	2	3	3	NUM
fcis-9856	107	3	lists	list	VERB
fcis-9856	107	4	the	the	DET
fcis-9856	107	5	quantitative	quantitative	ADJ
fcis-9856	107	6	evaluation	evaluation	NOUN
fcis-9856	107	7	metrics	metric	NOUN
fcis-9856	107	8	of	of	ADP
fcis-9856	107	9	the	the	DET
fcis-9856	107	10	models	model	NOUN
fcis-9856	107	11	on	on	ADP
fcis-9856	107	12	the	the	DET
fcis-9856	107	13	three	three	NUM
fcis-9856	107	14	different	different	ADJ
fcis-9856	107	15	training	training	NOUN
fcis-9856	107	16	sets	set	NOUN
fcis-9856	107	17	on	on	ADP
fcis-9856	107	18	the	the	DET
fcis-9856	107	19	a1sat2b	a1sat2b	PROPN
fcis-9856	107	20	test	test	NOUN
fcis-9856	107	21	set	set	VERB
fcis-9856	107	22	in	in	ADP
fcis-9856	107	23	turn	turn	NOUN
fcis-9856	107	24	.	.	PUNCT
fcis-9856	108	1	from	from	ADP
fcis-9856	108	2	the	the	DET
fcis-9856	108	3	results	result	NOUN
fcis-9856	108	4	,	,	PUNCT
fcis-9856	108	5	it	it	PRON
fcis-9856	108	6	can	can	AUX
fcis-9856	108	7	be	be	AUX
fcis-9856	108	8	seen	see	VERB
fcis-9856	108	9	that	that	SCONJ
fcis-9856	108	10	the	the	DET
fcis-9856	108	11	low	low	ADJ
fcis-9856	108	12	-	-	PUNCT
fcis-9856	108	13	resolution	resolution	NOUN
fcis-9856	108	14	images	image	NOUN
fcis-9856	108	15	obtained	obtain	VERB
fcis-9856	108	16	by	by	ADP
fcis-9856	108	17	bi	bi	ADJ
fcis-9856	108	18	-	-	ADJ
fcis-9856	108	19	trivial	trivial	ADJ
fcis-9856	108	20	interpolation	interpolation	NOUN
fcis-9856	108	21	have	have	VERB
fcis-9856	108	22	huge	huge	ADJ
fcis-9856	108	23	differences	difference	NOUN
fcis-9856	108	24	in	in	ADP
fcis-9856	108	25	feature	feature	NOUN
fcis-9856	108	26	distributions	distribution	NOUN
fcis-9856	108	27	from	from	ADP
fcis-9856	108	28	the	the	DET
fcis-9856	108	29	real	real	ADJ
fcis-9856	108	30	lowresolution	lowresolution	NOUN
fcis-9856	108	31	images	image	NOUN
fcis-9856	108	32	,	,	PUNCT
fcis-9856	108	33	resulting	result	VERB
fcis-9856	108	34	in	in	ADP
fcis-9856	108	35	their	their	PRON
fcis-9856	108	36	poorer	poor	ADJ
fcis-9856	108	37	results	result	NOUN
fcis-9856	108	38	on	on	ADP
fcis-9856	108	39	the	the	DET
fcis-9856	108	40	alsat2b	alsat2b	PROPN
fcis-9856	108	41	test	test	NOUN
fcis-9856	108	42	set	set	NOUN
fcis-9856	108	43	,	,	PUNCT
fcis-9856	108	44	while	while	SCONJ
fcis-9856	108	45	the	the	DET
fcis-9856	108	46	uc	uc	PROPN
fcis-9856	108	47	merced	merced	PROPN
fcis-9856	108	48	dataset	dataset	PROPN
fcis-9856	108	49	processed	process	VERB
fcis-9856	108	50	with	with	ADP
fcis-9856	108	51	the	the	DET
fcis-9856	108	52	degradation	degradation	NOUN
fcis-9856	108	53	model	model	NOUN
fcis-9856	108	54	of	of	ADP
fcis-9856	108	55	this	this	DET
fcis-9856	108	56	paper	paper	NOUN
fcis-9856	108	57	has	have	VERB
fcis-9856	108	58	similar	similar	ADJ
fcis-9856	108	59	feature	feature	NOUN
fcis-9856	108	60	distributions	distribution	NOUN
fcis-9856	108	61	to	to	ADP
fcis-9856	108	62	the	the	DET
fcis-9856	108	63	alsat2b	alsat2b	PROPN
fcis-9856	108	64	training	training	NOUN
fcis-9856	108	65	set	set	NOUN
fcis-9856	108	66	,	,	PUNCT
fcis-9856	108	67	so	so	CCONJ
fcis-9856	108	68	it	it	PRON
fcis-9856	108	69	is	be	AUX
fcis-9856	108	70	closer	close	ADJ
fcis-9856	108	71	in	in	ADP
fcis-9856	108	72	subjective	subjective	ADJ
fcis-9856	108	73	visual	visual	ADJ
fcis-9856	108	74	effects	effect	NOUN
fcis-9856	108	75	and	and	CCONJ
fcis-9856	108	76	quantitative	quantitative	ADJ
fcis-9856	108	77	evaluation	evaluation	NOUN
fcis-9856	108	78	indexes	index	NOUN
fcis-9856	108	79	,	,	PUNCT
fcis-9856	108	80	which	which	PRON
fcis-9856	108	81	also	also	ADV
fcis-9856	108	82	verifies	verify	VERB
fcis-9856	108	83	the	the	DET
fcis-9856	108	84	effectiveness	effectiveness	NOUN
fcis-9856	108	85	of	of	ADP
fcis-9856	108	86	the	the	DET
fcis-9856	108	87	degradation	degradation	NOUN
fcis-9856	108	88	model	model	NOUN
fcis-9856	108	89	is	be	AUX
fcis-9856	108	90	also	also	ADV
fcis-9856	108	91	verified	verify	VERB
fcis-9856	108	92	,	,	PUNCT
fcis-9856	108	93	and	and	CCONJ
fcis-9856	108	94	the	the	DET
fcis-9856	108	95	feature	feature	NOUN
fcis-9856	108	96	distribution	distribution	NOUN
fcis-9856	108	97	of	of	ADP
fcis-9856	108	98	the	the	DET
fcis-9856	108	99	generated	generate	VERB
fcis-9856	108	100	low	low	ADJ
fcis-9856	108	101	-	-	PUNCT
fcis-9856	108	102	resolution	resolution	NOUN
fcis-9856	108	103	images	image	NOUN
fcis-9856	108	104	is	be	AUX
fcis-9856	108	105	similar	similar	ADJ
fcis-9856	108	106	to	to	ADP
fcis-9856	108	107	that	that	PRON
fcis-9856	108	108	of	of	ADP
fcis-9856	108	109	the	the	DET
fcis-9856	108	110	real	real	ADJ
fcis-9856	108	111	lowresolution	lowresolution	NOUN
fcis-9856	108	112	images	image	NOUN
fcis-9856	108	113	.	.	PUNCT
fcis-9856	109	1	4	4	X
fcis-9856	109	2	.	.	X
fcis-9856	109	3	conclusion	conclusion	VERB
fcis-9856	109	4	the	the	DET
fcis-9856	109	5	paper	paper	NOUN
fcis-9856	109	6	addresses	address	VERB
fcis-9856	109	7	the	the	DET
fcis-9856	109	8	problem	problem	NOUN
fcis-9856	109	9	of	of	ADP
fcis-9856	109	10	poor	poor	ADJ
fcis-9856	109	11	recovery	recovery	NOUN
fcis-9856	109	12	effect	effect	NOUN
fcis-9856	109	13	of	of	ADP
fcis-9856	109	14	current	current	ADJ
fcis-9856	109	15	super	super	ADJ
fcis-9856	109	16	-	-	ADJ
fcis-9856	109	17	resolution	resolution	ADJ
fcis-9856	109	18	reconstruction	reconstruction	NOUN
fcis-9856	109	19	algorithms	algorithm	NOUN
fcis-9856	109	20	in	in	ADP
fcis-9856	109	21	the	the	DET
fcis-9856	109	22	field	field	NOUN
fcis-9856	109	23	of	of	ADP
fcis-9856	109	24	real	real	ADJ
fcis-9856	109	25	remote	remote	ADJ
fcis-9856	109	26	sensing	sensing	NOUN
fcis-9856	109	27	images	image	NOUN
fcis-9856	109	28	,	,	PUNCT
fcis-9856	109	29	and	and	CCONJ
fcis-9856	109	30	adopts	adopt	VERB
fcis-9856	109	31	the	the	DET
fcis-9856	109	32	method	method	NOUN
fcis-9856	109	33	of	of	ADP
fcis-9856	109	34	randomly	randomly	ADV
fcis-9856	109	35	mixing	mix	VERB
fcis-9856	109	36	and	and	CCONJ
fcis-9856	109	37	washing	washing	NOUN
fcis-9856	109	38	degradation	degradation	NOUN
fcis-9856	109	39	factors	factor	NOUN
fcis-9856	109	40	to	to	PART
fcis-9856	109	41	simulate	simulate	VERB
fcis-9856	109	42	the	the	DET
fcis-9856	109	43	imaging	imaging	NOUN
fcis-9856	109	44	process	process	NOUN
fcis-9856	109	45	of	of	ADP
fcis-9856	109	46	real	real	ADJ
fcis-9856	109	47	remote	remote	ADJ
fcis-9856	109	48	sensing	sensing	NOUN
fcis-9856	109	49	images	image	NOUN
fcis-9856	109	50	to	to	PART
fcis-9856	109	51	generate	generate	VERB
fcis-9856	109	52	low	low	ADJ
fcis-9856	109	53	-	-	PUNCT
fcis-9856	109	54	resolution	resolution	NOUN
fcis-9856	109	55	remote	remote	ADJ
fcis-9856	109	56	sensing	sensing	NOUN
fcis-9856	109	57	images	image	NOUN
fcis-9856	109	58	;	;	PUNCT
fcis-9856	109	59	meanwhile	meanwhile	ADV
fcis-9856	109	60	,	,	PUNCT
fcis-9856	109	61	a	a	DET
fcis-9856	109	62	superresolution	superresolution	NOUN
fcis-9856	109	63	reconstruction	reconstruction	NOUN
fcis-9856	109	64	algorithm	algorithm	NOUN
fcis-9856	109	65	based	base	VERB
fcis-9856	109	66	on	on	ADP
fcis-9856	109	67	generative	generative	ADJ
fcis-9856	109	68	adversarial	adversarial	ADJ
fcis-9856	109	69	network	network	NOUN
fcis-9856	109	70	is	be	AUX
fcis-9856	109	71	improved	improve	VERB
fcis-9856	109	72	to	to	PART
fcis-9856	109	73	enhance	enhance	VERB
fcis-9856	109	74	the	the	DET
fcis-9856	109	75	texture	texture	ADJ
fcis-9856	109	76	details	detail	NOUN
fcis-9856	109	77	of	of	ADP
fcis-9856	109	78	remote	remote	ADJ
fcis-9856	109	79	sensing	sensing	NOUN
fcis-9856	109	80	images	image	NOUN
fcis-9856	109	81	with	with	ADP
fcis-9856	109	82	resnet34	resnet34	NOUN
fcis-9856	109	83	and	and	CCONJ
fcis-9856	109	84	cnn	cnn	PROPN
fcis-9856	109	85	as	as	ADP
fcis-9856	109	86	the	the	DET
fcis-9856	109	87	basis	basis	NOUN
fcis-9856	109	88	,	,	PUNCT
fcis-9856	109	89	so	so	SCONJ
fcis-9856	109	90	as	as	SCONJ
fcis-9856	109	91	to	to	PART
fcis-9856	109	92	be	be	AUX
fcis-9856	109	93	able	able	ADJ
fcis-9856	109	94	to	to	PART
fcis-9856	109	95	extract	extract	VERB
fcis-9856	109	96	more	more	ADV
fcis-9856	109	97	rich	rich	ADJ
fcis-9856	109	98	features	feature	NOUN
fcis-9856	109	99	from	from	ADP
fcis-9856	109	100	images	image	NOUN
fcis-9856	109	101	to	to	PART
fcis-9856	109	102	extract	extract	VERB
fcis-9856	109	103	richer	rich	ADJ
fcis-9856	109	104	features	feature	NOUN
fcis-9856	109	105	,	,	PUNCT
fcis-9856	109	106	and	and	CCONJ
fcis-9856	109	107	the	the	DET
fcis-9856	109	108	effects	effect	NOUN
fcis-9856	109	109	of	of	ADP
fcis-9856	109	110	the	the	DET
fcis-9856	109	111	degradation	degradation	NOUN
fcis-9856	109	112	model	model	NOUN
fcis-9856	109	113	and	and	CCONJ
fcis-9856	109	114	super	super	ADJ
fcis-9856	109	115	-	-	ADJ
fcis-9856	109	116	resolution	resolution	ADJ
fcis-9856	109	117	reconstruction	reconstruction	NOUN
fcis-9856	109	118	model	model	NOUN
fcis-9856	109	119	are	be	AUX
fcis-9856	109	120	verified	verify	VERB
fcis-9856	109	121	on	on	ADP
fcis-9856	109	122	the	the	DET
fcis-9856	109	123	dataset	dataset	NOUN
fcis-9856	109	124	uc	uc	PROPN
fcis-9856	109	125	merced	merced	PROPN
fcis-9856	109	126	and	and	CCONJ
fcis-9856	109	127	the	the	DET
fcis-9856	109	128	real	real	ADJ
fcis-9856	109	129	remote	remote	ADJ
fcis-9856	109	130	sensing	sense	VERB
fcis-9856	109	131	dataset	dataset	VERB
fcis-9856	109	132	alsat2b	alsat2b	PROPN
fcis-9856	109	133	.	.	PUNCT
fcis-9856	110	1	by	by	ADP
fcis-9856	110	2	means	mean	NOUN
fcis-9856	110	3	of	of	ADP
fcis-9856	110	4	software	software	NOUN
fcis-9856	110	5	processing	processing	NOUN
fcis-9856	110	6	,	,	PUNCT
fcis-9856	110	7	the	the	DET
fcis-9856	110	8	resolution	resolution	NOUN
fcis-9856	110	9	of	of	ADP
fcis-9856	110	10	the	the	DET
fcis-9856	110	11	finished	finish	VERB
fcis-9856	110	12	commercial	commercial	ADJ
fcis-9856	110	13	remote	remote	ADJ
fcis-9856	110	14	sensing	sensing	NOUN
fcis-9856	110	15	images	image	NOUN
fcis-9856	110	16	is	be	AUX
fcis-9856	110	17	effectively	effectively	ADV
fcis-9856	110	18	enhanced	enhance	VERB
fcis-9856	110	19	,	,	PUNCT
fcis-9856	110	20	providing	provide	VERB
fcis-9856	110	21	higher	high	ADJ
fcis-9856	110	22	resolution	resolution	NOUN
fcis-9856	110	23	images	image	NOUN
fcis-9856	110	24	for	for	ADP
fcis-9856	110	25	tasks	task	NOUN
fcis-9856	110	26	such	such	ADJ
fcis-9856	110	27	as	as	ADP
fcis-9856	110	28	disaster	disaster	NOUN
fcis-9856	110	29	prediction	prediction	NOUN
fcis-9856	110	30	and	and	CCONJ
fcis-9856	110	31	target	target	NOUN
fcis-9856	110	32	detection	detection	NOUN
fcis-9856	110	33	.	.	PUNCT
fcis-9856	111	1	acknowledgments	acknowledgment	NOUN
fcis-9856	111	2	this	this	DET
fcis-9856	111	3	paper	paper	NOUN
fcis-9856	111	4	is	be	AUX
fcis-9856	111	5	supported	support	VERB
fcis-9856	111	6	by	by	ADP
fcis-9856	111	7	the	the	DET
fcis-9856	111	8	academic	academic	ADJ
fcis-9856	111	9	research	research	NOUN
fcis-9856	111	10	projects	project	NOUN
fcis-9856	111	11	of	of	ADP
fcis-9856	111	12	beijing	beijing	PROPN
fcis-9856	111	13	union	union	PROPN
fcis-9856	111	14	university	university	PROPN
fcis-9856	111	15	(	(	PUNCT
fcis-9856	111	16	no	no	INTJ
fcis-9856	111	17	.	.	PUNCT
fcis-9856	112	1	zk10202205	zk10202205	PROPN
fcis-9856	112	2	)	)	PUNCT
fcis-9856	112	3	.	.	PUNCT
fcis-9856	113	1	references	reference	NOUN
fcis-9856	113	2	[	[	X
fcis-9856	113	3	1	1	NUM
fcis-9856	113	4	]	]	X
fcis-9856	113	5	dong	dong	NOUN
fcis-9856	113	6	c	c	PROPN
fcis-9856	113	7	,	,	PUNCT
fcis-9856	113	8	loy	loy	PROPN
fcis-9856	113	9	c	c	PROPN
fcis-9856	113	10	c	c	X
fcis-9856	113	11	,	,	PUNCT
fcis-9856	113	12	he	he	PRON
fcis-9856	113	13	k	k	NOUN
fcis-9856	113	14	,	,	PUNCT
fcis-9856	113	15	et	et	PROPN
fcis-9856	113	16	al	al	PROPN
fcis-9856	113	17	.	.	PUNCT
fcis-9856	114	1	learning	learn	VERB
fcis-9856	114	2	a	a	DET
fcis-9856	114	3	deep	deep	ADJ
fcis-9856	114	4	convolutional	convolutional	ADJ
fcis-9856	114	5	network	network	NOUN
fcis-9856	114	6	for	for	ADP
fcis-9856	114	7	image	image	NOUN
fcis-9856	114	8	super	super	NOUN
fcis-9856	114	9	-	-	NOUN
fcis-9856	114	10	resolution	resolution	NOUN
fcis-9856	114	11	[	[	PUNCT
fcis-9856	114	12	c]//	c]//	PROPN
fcis-9856	114	13	lncs	lncs	PROPN
fcis-9856	114	14	8692	8692	NUM
fcis-9856	114	15	:	:	PUNCT
fcis-9856	114	16	proceedings	proceeding	NOUN
fcis-9856	114	17	of	of	ADP
fcis-9856	114	18	the	the	DET
fcis-9856	114	19	13th	13th	ADJ
fcis-9856	114	20	european	european	ADJ
fcis-9856	114	21	conference	conference	NOUN
fcis-9856	114	22	on	on	ADP
fcis-9856	114	23	computer	computer	NOUN
fcis-9856	114	24	vision	vision	NOUN
fcis-9856	114	25	,	,	PUNCT
fcis-9856	114	26	zurich	zurich	PROPN
fcis-9856	114	27	,	,	PUNCT
fcis-9856	114	28	sep	sep	PROPN
fcis-9856	114	29	6	6	NUM
fcis-9856	114	30	-	-	SYM
fcis-9856	114	31	12,2014.cham	12,2014.cham	NUM
fcis-9856	114	32	:	:	PUNCT
fcis-9856	114	33	springer	springer	NOUN
fcis-9856	114	34	,	,	PUNCT
fcis-9856	114	35	2014	2014	NUM
fcis-9856	114	36	:	:	PUNCT
fcis-9856	114	37	184	184	NUM
fcis-9856	114	38	-	-	SYM
fcis-9856	114	39	199	199	NUM
fcis-9856	114	40	.	.	PUNCT
fcis-9856	115	1	[	[	X
fcis-9856	115	2	2	2	X
fcis-9856	115	3	]	]	PUNCT
fcis-9856	115	4	kim	kim	PROPN
fcis-9856	115	5	j	j	PROPN
fcis-9856	115	6	,	,	PUNCT
fcis-9856	115	7	lee	lee	PROPN
fcis-9856	115	8	j	j	PROPN
fcis-9856	115	9	k	k	PROPN
fcis-9856	115	10	,	,	PUNCT
fcis-9856	115	11	lee	lee	PROPN
fcis-9856	115	12	k	k	PROPN
fcis-9856	115	13	m.	m.	PROPN
fcis-9856	115	14	accurate	accurate	PROPN
fcis-9856	115	15	image	image	NOUN
fcis-9856	115	16	super	super	NOUN
fcis-9856	115	17	-	-	NOUN
fcis-9856	115	18	resolution	resolution	NOUN
fcis-9856	115	19	using	use	VERB
fcis-9856	115	20	very	very	ADV
fcis-9856	115	21	deep	deep	ADJ
fcis-9856	115	22	convolutional	convolutional	ADJ
fcis-9856	115	23	networks[c]//proceedings	networks[c]//proceeding	NOUN
fcis-9856	115	24	of	of	ADP
fcis-9856	115	25	the	the	DET
fcis-9856	115	26	2016	2016	NUM
fcis-9856	115	27	ieee	ieee	NOUN
fcis-9856	115	28	conference	conference	NOUN
fcis-9856	115	29	on	on	ADP
fcis-9856	115	30	computer	computer	NOUN
fcis-9856	115	31	vision	vision	NOUN
fcis-9856	115	32	and	and	CCONJ
fcis-9856	115	33	pattern	pattern	NOUN
fcis-9856	115	34	recognition	recognition	NOUN
fcis-9856	115	35	,	,	PUNCT
fcis-9856	115	36	las	las	PROPN
fcis-9856	115	37	vegas	vegas	PROPN
fcis-9856	115	38	,	,	PUNCT
fcis-9856	115	39	jun	jun	PROPN
fcis-9856	115	40	27	27	NUM
fcis-9856	115	41	-	-	SYM
fcis-9856	115	42	30,2016.washington	30,2016.washington	NUM
fcis-9856	115	43	:	:	PUNCT
fcis-9856	115	44	ieee	ieee	NOUN
fcis-9856	115	45	computer	computer	NOUN
fcis-9856	115	46	society,2016:1646	society,2016:1646	PROPN
fcis-9856	115	47	-	-	SYM
fcis-9856	115	48	1654	1654	NUM
fcis-9856	115	49	.	.	PUNCT
fcis-9856	116	1	[	[	X
fcis-9856	116	2	3	3	X
fcis-9856	116	3	]	]	PUNCT
fcis-9856	116	4	he	he	PRON
fcis-9856	116	5	k	k	PROPN
fcis-9856	116	6	,	,	PUNCT
fcis-9856	116	7	zhang	zhang	PROPN
fcis-9856	116	8	x	x	PROPN
fcis-9856	116	9	,	,	PUNCT
fcis-9856	116	10	ren	ren	PROPN
fcis-9856	116	11	s	s	PROPN
fcis-9856	116	12	,	,	PUNCT
fcis-9856	116	13	et	et	NOUN
fcis-9856	116	14	al.deep	al.deep	VERB
fcis-9856	116	15	residual	residual	ADJ
fcis-9856	116	16	learning	learning	NOUN
fcis-9856	116	17	for	for	ADP
fcis-9856	116	18	image	image	NOUN
fcis-9856	116	19	recognition	recognition	NOUN
fcis-9856	117	1	[	[	X
fcis-9856	117	2	c]//	c]//	ADJ
fcis-9856	117	3	proceedings	proceeding	NOUN
fcis-9856	117	4	of	of	ADP
fcis-9856	117	5	the	the	DET
fcis-9856	117	6	2016	2016	NUM
fcis-9856	117	7	ieee	ieee	NOUN
fcis-9856	117	8	conference	conference	NOUN
fcis-9856	117	9	on	on	ADP
fcis-9856	117	10	computer	computer	NOUN
fcis-9856	117	11	vision	vision	NOUN
fcis-9856	117	12	and	and	CCONJ
fcis-9856	117	13	pattern	pattern	NOUN
fcis-9856	117	14	recognition	recognition	NOUN
fcis-9856	117	15	,	,	PUNCT
fcis-9856	117	16	lasvegas	lasvegas	PROPN
fcis-9856	117	17	,	,	PUNCT
fcis-9856	117	18	jun	jun	PROPN
fcis-9856	117	19	27	27	NUM
fcis-9856	117	20	-	-	SYM
fcis-9856	117	21	30	30	NUM
fcis-9856	117	22	,	,	PUNCT
fcis-9856	117	23	2016.washington	2016.washington	NUM
fcis-9856	117	24	:	:	PUNCT
fcis-9856	117	25	ieee	ieee	NOUN
fcis-9856	117	26	computer	computer	NOUN
fcis-9856	117	27	society	society	NOUN
fcis-9856	117	28	,	,	PUNCT
fcis-9856	117	29	2016	2016	NUM
fcis-9856	117	30	:	:	PUNCT
fcis-9856	117	31	770	770	NUM
fcis-9856	117	32	-	-	SYM
fcis-9856	117	33	778	778	NUM
fcis-9856	117	34	.	.	PUNCT
fcis-9856	118	1	[	[	X
fcis-9856	118	2	4	4	NUM
fcis-9856	118	3	]	]	X
fcis-9856	118	4	goodfellow	goodfellow	PROPN
fcis-9856	118	5	i	i	PROPN
fcis-9856	118	6	,	,	PUNCT
fcis-9856	118	7	pouget	pouget	NOUN
fcis-9856	118	8	-	-	PUNCT
fcis-9856	118	9	abadie	abadie	NOUN
fcis-9856	118	10	j	j	PROPN
fcis-9856	118	11	,	,	PUNCT
fcis-9856	118	12	mirza	mirza	PROPN
fcis-9856	118	13	m	m	PROPN
fcis-9856	118	14	,	,	PUNCT
fcis-9856	118	15	et	et	PROPN
fcis-9856	118	16	al	al	PROPN
fcis-9856	118	17	.	.	PROPN
fcis-9856	118	18	generative	generative	ADJ
fcis-9856	118	19	adversarial	adversarial	ADJ
fcis-9856	118	20	networks[c]//proceedings	networks[c]//proceeding	NOUN
fcis-9856	118	21	of	of	ADP
fcis-9856	118	22	the	the	DET
fcis-9856	118	23	annual	annual	ADJ
fcis-9856	118	24	conference	conference	NOUN
fcis-9856	118	25	on	on	ADP
fcis-9856	118	26	neural	neural	ADJ
fcis-9856	118	27	information	information	NOUN
fcis-9856	118	28	processing	processing	NOUN
fcis-9856	118	29	systems	system	NOUN
fcis-9856	118	30	2014,montreal	2014,montreal	PROPN
fcis-9856	118	31	,	,	PUNCT
fcis-9856	118	32	dec	dec	PROPN
fcis-9856	118	33	8	8	NUM
fcis-9856	118	34	-	-	SYM
fcis-9856	118	35	13,2014.red	13,2014.red	NUM
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fcis-9856	121	47	.	.	PUNCT
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fcis-9856	127	30	.	.	PUNCT
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fcis-9856	130	1	[	[	X
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