A new effective denoising filter for high density impulse noise reduction

Today, thanks to the rapid development of technology, the importance of digital images is increasing. However, sensor errors that may occur during the acquisition, interruptions in the transmission of images and errors in storage cause noise that degrades data quality. Salt and pepper noise, a common impulse noise, is one of the most well-known types of noise in digital images. This noise negatively affects the detailed analysis of the image. It is very important that pixels affected by noise are restored without loss of image fine details, especially at high level of noise density. Although many filtering algorithms have been proposed to remove noise, the enhancement of images with high noise levels is still complex, not efficient or requires very long runtime. In this paper, we propose an effective denoising filter that can restore the image effectively in terms of quality and speed with less complexity for high density noise level. In the experimental studies, we compare the denoising results of the proposed method with other state-of-the-art methods and the proposed algorithm is quantitatively and visually comparable to these algorithms when the noise intensity is up to 90%.

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[1] Gonzalez RC, Woods RE. Digital Image Processing, 3rd ed, Prentice Hall: Upper Saddle River, NJ, USA, 2008.
[2] Mafi M, Rajaei H, Cabrerizo M, Adjouadi M. A robust edge detection approach in the presence of high impulse noise intensity through switching adaptive median and fixed weighted mean filtering. IEEE Transactions on Image Processing 2018; 27 (11): 5475-5490 doi: 10.1109/TIP.2018.2857448
[3] Fu B, Zhao X, Song C, Li X, Wang X. A salt and pepper noise image denoising method based on the generative classification. Multimedia Tools and App. 2019; 78 (9): 12043–12053. doi: 10.1007/s11042-018-6732-8
[4] White PR, Baumann O, DeStefano A. On improving the noise reduction algorithms using image segmentation. In: Proceedings of European Signal Processing Conference (EUSIPCO); 2004, pp. 501–504
[5] Ou Q, Zhang H, Li L, Xiong B. Impulse noise removal in two-dimensional electrophoresis images based on dome recognition. In: IEEE 2015 8th International Conference on Biomedical Engineering and Informatics (BMEI); Shenyang; 2015. pp. 440-445
[6] Pratt WK. Median Filtering, Technology Rep, Image Processing Institute University of Southern California: Los Angeles, CA, USA, 1975
[7] Hwang H, Haddad RA. Adaptive median filters: new algorithms and results. IEEE Transactions on Image Processing 1995; 4 (4): 499–502. doi: 10.1109/83.370679
[8] Yin L, Yang R, Gabbouj M, Neuvo Y. Weighted median filters: a tutorial. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 1996; 43 (3): 157–192. doi: 10.1109/82.486465
[9] Ko SJ, Lee YH. Center weighted median filters and their applications to image enhancement. IEEE Transactions on Circuits and Systems 1991; 38 (9): 984–993. doi: 10.1109/31.83870
[10] Pattnaik A, Agarwal S, Chand S. A new and efficient method for removal of high density salt and pepper noise through cascade decision based filtering algorithm. In: 2nd International Conference on Communication, Computing & Security, Procedia Technology, 2012, 6, pp. 108–117
[11] Chan RH, Chung-Wa H, Nikolova M. Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization. IEEE Transactions on Image Processing 2005; 14 (10): 1479-1485. doi: 10.1109/TIP.2005.852196
[12] Sun T, Neuvo Y. Detail-preserving median based filters in image processing. Pattern Recognition Letters 1994; 15 (4): 341–347. doi: 10.1016/0167-8655(94)90082-5
[13] Wang Z, Zhang D. Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 1999; 46 (1): 78-80. doi: 10.1109/82.749102
[14] Kang CC, Wang WJ. Modified switching median filter with one more noise detector for impulse noise removal. AEU - International Journal of Electronics and Communications 2009; 63 (11): 998-1004. doi: 10.1016/j.aeue.2008.08.009
[15] Akkoul S, Ledee R, Leconge R, Harba R. A new adaptive switching median filter. IEEE Signal Processing Letters 2010; 17 (6): 587-590. doi: 10.1109/LSP.2010.2048646
[16] Faragallah OS, Ibrahem HM. Adaptive switching weighted median filter framework for suppressing salt-andpepper noise. AEU - International Journal of Electronics and Communications 2016; 70 (8): 1034-1040. doi: 10.1016/j.aeue.2016.04.018
[17] Zhang X, Xiong Y. Impulse noise removal using directional difference based noise detector and adaptive weighted mean filter. IEEE Signal Processing Letters 2009; 16 (4): 295-298. doi: 10.1109/LSP.2009.2014293
[18] Dong Y, Xu S. A new directional weighted median filter for removal of random-valued impulse noise. IEEE Signal Processing Letters 2007; 14 (3): 193–196. doi: 10.1109/LSP.2006.884014
[19] Deivalakshmi S, Sarath S, Palanisamy P. Detection and removal of salt and pepper noise in images by improved median filter. In: IEEE 2011 Recent Advances in Intelligent Computational Systems; 2011, pp. 363-368.
[20] Zhang P, Li F. A new adaptive weighted mean filter for removing salt-and-pepper noise. IEEE Signal Processing Letters 2014; 21 (10): 1280-1283. doi: 10.1109/LSP.2014.2333012
[21] Saleem SA, Razak TA. An effective noise adaptive median filter for removing high density impulse noises in color images. International Journal of Electrical and Computer Engineering (IJECE) 2016; 6 (2): 611-620. doi: 10.11591/ijece.v6i2.pp 611-620
[22] Lu CT, Chou TC. Denoising of salt-and-pepper noise corrupted image using modified directional-weighted-median filter. Pattern Recognition Letters 2012; 33 (10): 1287–1295. doi: 10.1016/j.patrec.2012.03.025
[23] Esakkirajan S, Veerakumar T, Subramanyam AN, PremChand CH. Removal of high density salt and pepper noise through modified decision based unsymmetric trimmed median filter. IEEE Signal Processing Letters 2011; 18 (5): 287-290. doi: 10.1109/LSP.2011.2122333
[24] Erkan U, Gokrem L. A new method based on pixel density in salt and pepper noise removal. Turkish J. Electr. Eng. Comput. Sci. 2018; 26, 162-171. doi: 10.3906/elk-1705-256
[25] Toh KKV, Isa NAM. Noise adaptive fuzzy switching median filter for salt-and-pepper noise reduction. IEEE Signal Processing Letters 2010; 17 (3): 281-284. doi: 10.1109/LSP.2009.2038769
[26] Kiani V, Zohrevand A. A fuzzy directional median filter for fixed-value impulse noise removal. In: IEEE 2019 7th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS); Bojnord, Iran, 2019, pp. 1-4.
27] Karthik B, Kumar TK, Vijayaragavan SP, Sriram M. Removal of high density salt and pepper noise in color image through modified cascaded filter. Journal of Ambient Intelligence and Humanized Computing 2021; 12 (3): 3901-3908. doi: 10.1007/s12652-020-01737-1
[28] Singh V, Dev R, Dhar NK, Agrawal P, Verma NK. Adaptive type-2 fuzzy approach for filtering salt and pepper noise in grayscale images. IEEE Transactions on Fuzzy Systems 2018; 26 (5): 3170-3176. doi: 10.1109/TFUZZ.2018.2805289
[29] Varghese J, Tairan N, Subash S. Adaptive switching non-local filter for the restoration of salt and pepper impulse-corrupted digital images. Arabian Journal for Science and Engineering 2015; 40 (11): 3233-3246. doi: 10.1007/s13369-015-1799-2
[30] Zhang C, Wang K. A switching median–mean filter for removal of high-density impulse noise from digital images. Optik 2015; 126 (9-10): 956-961. doi: 10.1016/j.ijleo.2015.02.085
[31] Balasubramanian G, Chilambuchelvan A, Vijayan S, Gowrison G. Probabilistic decision based filter to remove impulse noise using patch else trimmed median. AEU - International Journal of Electronics and Communications 2016; 70 (4): 471-481. doi: 10.1016/j.aeue.2016.01.013
[32] Erkan U, Gökrem L, Enginoğlu S. Different applied median filter in salt and pepper noise. Computers & Electrical Engineering 2018; 70, 789-798. doi: 10.1016/j.compeleceng.2018.01.019
[33] Varatharajan R, Vasanth K, Gunasekaran M, Priyan M, Gao XZ. An adaptive decision based kriging interpolation algorithm for the removal of high density salt and pepper noise in images. Computers & Electrical Engineering 2018; 70, 447-461. doi: 10.1016/j.compeleceng.2017.05.035
[34] Enginoglu S, Erkan U, Memis S. Pixel similarity-based adaptive riesz mean filter for salt-and-pepper noise removal. Multimedia Tools and Applications 2019; 78 (24): 35401-35418. doi: 10.1007/s11042-019-08110-1
[35] Enginoğlu S, Erkan U, Memiş S. Adaptive cesáro mean filter for salt-and-pepper noise removal. El-Cezeri 2020; 7, 304-314. doi: 10.31202/ecjse.646359.
[36] Erkan U, Thanh DNH, Enginoğlu S, Memiş S. Improved adaptive weighted mean filter for salt-and-pepper noise removal. In: ICECCE 2020 International Conference on Electrical, Communication, and Computer Engineering, 2020, pp. 1-5.
[37] Memiş S, Erkan U. Different adaptive modified riesz mean filter for high-density salt-and-pepper noise removal in grayscale images. Avrupa Bilim ve Teknoloji Dergisi 2021; 359-367. doi: 10.31590/ejosat.873312
[38] Erkan U, Enginoğlu S, Thanh DNH, Hieu LM. Adaptive frequency median filter for the salt and pepper denoising problem. IET Image Processing 2020; 14 (7): 1291-1302. doi: 10.1049/iet-ipr.2019.0398
[39] Aghajarian M, McInroy JE, Wright CHG. Salt-and-pepper noise removal using modified mean filter and total variation minimization. Journal of Electronic Imaging 2018; 27 (1), doi: 10.1117/1.JEI.27.1.013002
[40] Thanh DNH, Surya PVB, Thanh LT. Total variation l1 fidelity salt-and-pepper denoising with adaptive regularization parameter. In: IEEE 2018 5th NAFOSTED Conference on Information and Computer Science (NICS); Ho Chi Minh City, 2018, pp. 400–405.
[41] Xing Y, Xu J, Tan J, Li D, Zha W. Deep CNN for removal of salt and pepper noise. IET Image Processing 2019; 13 (9): 1550-1560. doi: 10.1049/iet-ipr.2018.6004
[42] Jin L, Zhang W, Ma G, Song E. Learning deep CNNs for impulse noise removal in images. Journal of Visual Communication and Image Representation 2019; 62, 193-205. doi: 10.1016/j.jvcir.2019.05.005
[43] Sadrizadeh S, Zarmehi N, Kangarshahi EA, Abin H, Marvasti, F. A fast iterative method for removing impulsive noise from sparse signals. IEEE Transactions on Circuits and Systems for Video Technology 2021; 31 (1): 38-48. doi: 10.1109/TCSVT.2020.2969563
[44] Sen AP, Rout, NK. Improved probabilistic decision-based trimmed median filter for detection and removal of highdensity impulsive noise. IET Image Processing 2020; 14 (17): 4486-4498. doi: 10.1049/iet-ipr.2019.1240
[45] Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 2004; 13 (4): 600-612. doi: 10.1109/TIP.2003.819861
[46] Asuni N, Giachetti A. TESTIMAGES: a large-scale archive for testing visual devices and basic image processing algorithms. In: Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference, Giachetti, A, Ed, The Eurographics Association, 2014.