International Journal of Interactive Mobile Technologies (iJIM) – eISSN: 1865-7923 – Vol 17 No 04 (2023) Short Paper—Enhance Mobile Phone Digital Camera Images Using Band Reject Filter and Discrete… Enhance Mobile Phone Digital Camera Images Using Band Reject Filter and Discrete Fourier Transform https://doi.org/10.3991/ijim.v17i04.37669 Rusul H. Al_taie1(), Hawraa abd Al_kadum hassan2 1 College of Arts, University of Babylon, Hillah, Iraq 2 College of information technology, University of Babylon, Hillah, Iraq rusul.jasem@uobabylon.edu.iq Abstract—These days, We know that mobile phones with camera are very common to used by most of the people because of their ease of use and carry. So I figured I need for mobile phone images processing. These images are often subjected to a variety of types of distortion, such as noise, poor illumination, and blur due to transmission, image acquisition and mobile movement during capturing scene. This Degradation leads to produce distorted images are not suitable for viewing or application. To overcome this degradation, this paper presents image improvement in mobile phone by distortion removal using Band Reject Filter and DFT to obtain on the best clear image. Periodic noise is sinusoidal of several of certain frequencies and periodic in nature. It looks identical to the bars that the image is covered. The proposed method is subjected to eliminate this noise from a set of images using Band Reject Filter using discrete Fourier transform that attenuate and replace a number of degradation bands. Then, computes the peak signal to noise ratio metric for each band. After that, select the largest value of each band for the purpose the making one clear image. Finally, the bands that carry value large are merged to produce a one image. The final image is converted to the spatial domain using an inverse Fourier transform. The experimental outcomes indicate that the proposed method improves the degraded image by a greater percentage without using the better value for metric and makes image more clear and informative compare with the input image. Keyword—Band Reject Filter (BRF), periodic noise, image enhancement, DFT, mobile phone images 1 Introduction Distortion is unwanted information about the image or an error in the pixel value of an image by a peripheral external operation (Russell, 2019). Usually periodic noise is caused by electrical or electromechanic overlaps. It is analogous to the set of bars as in Figure 1. It is one of the structural noise has general effects of degradation. This type of noise occurs because equipments of image or network and external effects of nature. This noise effect can be decreased using two dimensional discrete Fourier transform (Varghese et.al, 2020). When the frequency is low of periodic noise, the 134 http://www.i-jim.org https://doi.org/10.3991/ijim.v17i04.37669 Short Paper—Enhance Mobile Phone Digital Camera Images Using Band Reject Filter and Discrete… points may be close to the center of the image as well as vice vers. Band Stop Filter is also called as Notch Filter, frequency blocking and reject them that located in two cutoff frequency arguments (Hamd, 2014). Low-pass RC filter is mereg with high- pass RC filter to configure simple band pass filter which passes a frequencies of ranges. These filters are combined to create Band Reject Filter that block range of frequencies. If Band Reject Filter very narrow then it is referred notch filter it is more universally (Grossi et.al, 2016; Alibabaie & Latif, 2019). Original image Original image with periodic noise ripples Fig. 1. Original image and noise image (Ionita et.al , 2017) The band rejection filter function passes all frequencies starting with zero to the first and lowest frequency and passes these frequencies over it which is the second upper frequency. It blocks all these frequencies in between. Whenever noise location is known, Band Reject Filter is useful. It blocks frequencies in the certain range while frequencies outside of the range is let to pass through (Hassan et.al, 2022). Band Reje ct Filter removes periodic noise from the image as in Figure 2. There are two types of it (Wide band reject) filter and (Narrow band reject) filter (Notch Filter). These noises are commonly in several applications such as medical applications, remote sensing and television applications (Ketenci & Gangal, 2017; Hamd, 2014). iJIM ‒ Vol. 17, No. 04, 2023 135 Short Paper—Enhance Mobile Phone Digital Camera Images Using Band Reject Filter and Discrete… Original image Filtered image Fig. 2. Original image and filtered image (Chakraborty et.al, 2016) Image enhancement denotes the process of emphasizing definite information of an image as well as eliminating unnecessary or redundant information such as eliminating noise, removing blurred, and etc. Image enhancement is applied to improve the image vision for human visual. Images is degraded by different noise types such as salt & pepper ,Gaussian, speckle noises and blur (Rusul, 2021). Image combination is a process for collecting all essential information from several images and producing single image is more accurate and informative than source images. The purpose of it is creating image that is more unstandable for human and machine (Ali and Rusul, 2016). It is performed on spatial domain and transform domain. For example, Averaging, principal component analysis and Brovey method are spatial domain methods while discrete wavelet and discrete Fourier transforms are frequency domain (Zahraa et.al, 2021; Flusser et.al, 2007). This paper presents a method to enhance an image by BRF and DFT. 2 Mobile phone image noise Key camera factors influence final image quality is the size of the image sensor. Larger megapixel sensors have smaller individual pixel sizes, which in practice increases the spatial images resolution and density of the sample that get it by a a lens with limited focal length, also noise levels up, which makes the sensitivity of the real Light is decreased. Because of the presence of these limitations, a trade-off occurs between light sensitivity and spatial resolution. At more lower megapixels can be achieve the optimal performance. the digital camera processor amplifies the electrical signal, resulting in a higher equal level of noise . 136 http://www.i-jim.org Short Paper—Enhance Mobile Phone Digital Camera Images Using Band Reject Filter and Discrete… Regrettably, noise with high sensitivity cannot be separated while one always accompanies the other. As the temperature of the sensor rises, so does noise. 3 The method A useful method is presented throughout this paper for image optimization using Reject Band filter & DFT for mobile phone images. In the beginning, set of various frequencies of noisy in the images. Then, 2D Discrete Fourier transform of images are calculated according to Equation (1) as in Figure 3. Reject Band filter applied on images that contains noise to delete it from images. After that, peak signal to noise ratio is computed for each band. The largest value of the metric is taken from all image then merged it to become signal image that more informative from original images. The inverse DFT is performed on a result image in order to transform it to spatial domain after she was in frequency domain to construct an enhanced image as in Equation (2). Figure 4 shows proposed method that clarifies the method. (1) (2) Fig. 3. 2D Discrete Fourier transform of the input image that contains periodic noise iJIM ‒ Vol. 17, No. 04, 2023 137 Short Paper—Enhance Mobile Phone Digital Camera Images Using Band Reject Filter and Discrete… Fig. 4. Proposed Method Diagram 4 Results The experimental results are performed in Visual Basic 2012 software. Three images are tested by different frequencies of periodic noise. These images are the same size and same scene. The goal comparison between various algorithms is executed with (Peak Signal to Noise Ratio PSNR), (Mean Absolute Error MAE) and (Computation Time (CT)) at second measures. First, Discrete Fourier transform is applied to these images to transform images from a spatial domain to frequency domain. Second, Band Reject Filter is performed to eliminate noise from images. Figure 5 illuminates input noisy bird images. It presents periodic noise removal from images using BRF and improved image is formed from taking value larger of PSNR Apply Reject Band filter using 2D DFT on images Compute PSNR for each band Select PSNR larger from each band Synthesis image process Set of input images Periodic noise Noisy images Apply inverse Fourier transform improved image 138 http://www.i-jim.org Short Paper—Enhance Mobile Phone Digital Camera Images Using Band Reject Filter and Discrete… measure. The larger value of PSNR of each image is taken to form improved image. The enhanced image is transformed from frequency domain to spatial domain to build final image. The efficiency of this proposed method has been compared with Windowed Gaussian Notch Filter (WGNF) (Aizenberg & Butakoff 2008) and Adaptive threshold based filter (ATBF) (Varghese 2016). A good method gives high value of PSNR and low values of MAE and CT. Image1 with 0.3 strength noise Image2 with 0.5 strength noise Image3 with 0.8 strength noise Noise removal of image1 Noise removal of image2 Noise removal of image3 Improved image Fig. 5. Noisy images, Denoised images and improved image PSNR and MAE are calculated according to equations (3,4), Where Z original image and O improved image. (3) (4) iJIM ‒ Vol. 17, No. 04, 2023 139 Short Paper—Enhance Mobile Phone Digital Camera Images Using Band Reject Filter and Discrete… Table 1 offers comparisons between different algorithms for several measures are PSNR, MAE and CT. These images are degraded with different amounts of noise and are eliminated by Band Reject Filter. Table 1. Comparative PSNR, MAE and CT Produced by different Algorithm of birds images corrupted with various noise strength Figure 6 shows original noisy Barbara images. Band Reject Filter are applied on the images to attenuate noise from them and then merged image is formed through the taking larger value of PSNR measure. This Figure presents periodic noise elimination from images using Band Reject Filter. The larger value of PSNR of each image is joined to construct an improved image. The final image is transformed to spatial domain using inverse discrete Fourier transform (IDFT) to construct an enhanced image. Image1 with 0.3 strength noise Image2 with 0.5 strength noise Image3 with 0.8 strength noise Noise removal of image1 Noise removal of image2 Noise removal of image3 Denoising method Noise strength 0.3 Noise strength 0.5 Noise strength 0.8 PSNR CT MAE PSNR CT MAE PSNR CT MAE WGNF 25.18 12.22 4.18 17.88 12.32 15.35 13.45 12.42 29.34 ATBF 32.23 4.2 2.34 22.8 4.1 9.23 17.43 4.3 17.56 Proposed method 37.43 1.71 1.23 34.23 2.68 5.20 26.5 1.78 7.46 140 http://www.i-jim.org Short Paper—Enhance Mobile Phone Digital Camera Images Using Band Reject Filter and Discrete… Improved image Fig. 6. Noisy images, Denoised images and Improved image Table 2 presents comparisons between different equality measures for several algorithms are WGNF, BRF with the proposed method. The images in Table 2 are corrupted with different strengths of noise and are removed using BRF. Table 2. Compartive PSNR, MAE and CT produced by different Algorithms of Barbara Images corrupted with various noise strength Denoising method Noise strength 0.3 Noise strength 0.5 Noise strength 0.8 PSNR CT MAE PSNR CT MAE PSNR CT MAE WGNF 23.16 11.22 3.17 15.82 12.26 13.33 11.35 11.24 27.24 ATBF 30.21 4.1 2.36 22.8 4.1 7.25 17.43 4.3 16.36 Proposed method 38.33 1.74 1.21 33.21 1.66 4.23 24.7 1.72 7.43 5 Conclusions In this proposed, Band Reject Filter was proposed to remove periodic noise from mobile phone images at domain of the frequancy. The techniques of spatial domain are good to remove random noise while the methods of frequency domain are suitable to eliminate periodic noise. The blending process with Band Reject Filter are given more clear image and quality better from original images. The improved image is built depend on the Peak signal noise ratio. The larger value for the measure is taken to form a merged image and then it is transformed to spatial domain to construct an improved image. 6 References [1] Aizenberg, I., & Butakoff, C. (2008). A windowed Gaussian notch filter for quasi-periodic noise removal. Image and Vision Computing, 26(10), 1347-1353. https://doi.org/10.1016/ j.imavis.2007.08.011 [2] Alibabaie, N., & Latif, A. M. (2019). Fuzzy Notch Filter for Periodic and Quasi-Periodic Noise Reduction in Digital Images. Journal of Machine Vision and Image Processing. [3] Chakraborty, D., Tarafder, M. K., Chakraborty, A., & Banerjee, A. (2016). A proficient method for periodic and quasi-periodic noise fading using spectral histogram thresholding iJIM ‒ Vol. 17, No. 04, 2023 141 https://doi.org/10.1016/j.imavis.2007.08.011 https://doi.org/10.1016/j.imavis.2007.08.011 Short Paper—Enhance Mobile Phone Digital Camera Images Using Band Reject Filter and Discrete… with sinc restoration filter. AEU-international journal of electronics and communications, 70(12), 1580-1592. https://doi.org/10.1016/j.aeue.2016.09.003 [4] Flusser, J., Sroubek, F., & Zitova, B. (2007). Image fusion: principles, methods, and applications. Tutorial Eusipco, 5, 1-60. [5] Grossi, E., Lops, M., & Venturino, L. (2016). A new look at the radar detection problem. IEEE Transactions on Signal Processing, 64(22), 5835-5847. https://doi.org/10.1109/TSP. 2016.2598312 [6] Hamd, M. (2014). RGB Image Reconstruction Using Two-Separated Band Reject Filters. Advances in Image and Video Processing, 2(2), 1-7. https://doi.org/10.14738/aivp.22.143 [7] Hassan, H. abd A., Zahraa Yaseen Hasan, & Rusul H. Al Taie. (2022). A Simulation Approach to Improve the VANETs Communication. International Journal of Interactive Mobile Technologies (iJIM), 16(12), pp. 137–144. https://doi.org/10.3991/ijim.v16i12. 31423 [8] Ionita, M. G., & Coanda, H. G. (2017, July). Automatic periodic noise removal in microscopy images. In 2017 International Symposium on Signals, Circuits and Systems (ISSCS) (pp. 1-4). IEEE. https://doi.org/10.1109/ISSCS.2017.8034894 [9] Israa Hadi Ali and Russell H. Al_Taie, 2016. Image Deblurring Using Fusion Technique Based on Inverse Filtering. Research Journal of Applied Sciences, 11: 1206-1210. [10] Israa Hadi Ali and Russell H. Al_taie, 2016. Wavelet Coefficient Fusion Method -Based Image Denoising. Research Journal of Applied Sciences, 11: 1045-1049. [11] Ketenci, S., & Gangal, A. (2017). Automatic reduction of periodic noise in images using adaptive Gaussian star filter. Turkish Journal of Electrical Engineering and Computer Science, 25(3), 2336-2348. https://doi.org/10.3906/elk-1506-78 [12] Russell, A. H. (2019, September). Noise removal from medical image using fusion technique based on DWT coefficient. In Journal of Physics: Conference Series (Vol. 1294, No. 2, p. 022015). IOP Publishing. https://doi.org/10.1088/1742-6596/1294/2/022015 [13] Rusul H. Al_taie. (2021). Restoration for blurred noisy images based on guided filtering and inverse filter. International Journal of Electrical and Computer Engineering (IJECE), 11(2), 1265-1275. https://doi.org/10.11591/ijece.v11i2.pp1265-1275 [14] Varghese, J., Subhash, S., Subramaniam, K., & Sridhar, K. P. (2020). Adaptive Gaussian notch filter for removing periodic noise from digital images. IET Image Processing, 14(8), 1529-1538. https://doi.org/10.1049/iet-ipr.2018.5707 [15] Varghese, J. (2016). Adaptive threshold based frequency domain filter for periodic noise reduction. AEU-international journal of electronics and communications, 70(12), 1692- 1701. https://doi.org/10.1016/j.aeue.2016.10.008 [16] Zahraa Yaseen Hasan, Rusul H. Al_taie, Hawraa abd Al_kadum Hassan,(2021). Fusion for medical image based on discrete wavelet transform coefficient" , Indonesian Journal of Electrical Engineering and Computer Science, Vol. 21, No. 3, pp. 1407-1416. https://doi.org/10.11591/ijeecs.v21.i3 7 Authors Rusul H. Al Taie is an assistant teacher and completed a Bachelor’s degree in Computer Science from college of science for women University of Babylon in 2010, and I obtained a master’s degree in Software Department from Information Technology college at the University of Babylon in 2017. I work at the Faculty of Arts at the University of Babylon and I study Computer for the first and second stages (Email: rusul.jasem@uobabylon.edu.iq). 142 http://www.i-jim.org https://doi.org/10.1016/j.aeue.2016.09.003 https://doi.org/10.1109/TSP.2016.2598312 https://doi.org/10.1109/TSP.2016.2598312 https://doi.org/10.14738/aivp.22.143 https://doi.org/10.3991/ijim.v16i12.31423 https://doi.org/10.3991/ijim.v16i12.31423 https://doi.org/10.1109/ISSCS.2017.8034894 https://doi.org/10.3906/elk-1506-78 https://doi.org/10.1088/1742-6596/1294/2/022015 https://doi.org/10.11591/ijece.v11i2.pp1265-1275 https://doi.org/10.1049/iet-ipr.2018.5707 https://doi.org/10.1016/j.aeue.2016.10.008 https://doi.org/10.11591/ijeecs.v21.i3 Short Paper—Enhance Mobile Phone Digital Camera Images Using Band Reject Filter and Discrete… Hawraa abd Al_kadum Hassan received the Bachelor’s degree in 2010 from Babylon University/College of Science—Department of Computer Science. She received a master’s degree in 2017 from the University of Babylon/College of Information Technology. She is currently working as an Assistant teacher at the University of Babylon/College of Information Technology/software Department. For the fourth stage of computing security, Operation Research (Email: hawraa.abd@ uobabylon.edu.iq). Article submitted 2022-11-26. Resubmitted 2022-12-30. Final acceptance 2023-01-02. Final version published as submitted by the authors. iJIM ‒ Vol. 17, No. 04, 2023 143 mailto:hawraa.abd@uobabylon.edu.iq mailto:hawraa.abd@uobabylon.edu.iq