id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
fcis-9856	Chen, Wei; Shen, Haiwei	A Super-resolution Algorithm for Remote Sensing Images based on Data Enhancement and Generative Adversarial Networks	2023	5	.pdf	application/pdf	4118	162	38	The degradation model uses randomly mixing and washing degradation factors (such as blur, downsampling, noise, etc.) to simulate the real imaging process to generate realistic low-resolution images combined with the original images in pairs for training; meanwhile, a new image processing architecture based on generative adversarial networks is proposed to enhance image details and preserve image features through ResNet34 and mini-CNN networks, and the method can better enhance the effect of super-resolution reconstruction of remote sensing images. The paper addresses the problem of poor recovery effect of current super-resolution reconstruction algorithms in the field of real remote sensing images, and adopts the method of randomly mixing and washing degradation factors to simulate the imaging process of real remote sensing images to generate low-resolution remote sensing images; meanwhile, a super- resolution reconstruction algorithm based on generative adversarial network is improved to enhance the texture details of remote sensing images with ResNet34 and CNN as the basis, so as to be able to extract more rich features from images to extract richer features, and the effects of the degradation model and super-resolution reconstruction model are verified on the dataset UC Merced and the real remote sensing dataset Alsat2B.	cache/fcis-9856.pdf	txt/fcis-9856.txt