Abstract
In this paper we propose a multistage selective convolution filter (MSCF) for fast and efficient removal of salt-and-pepper noise (SPN) in digital images. By avoiding the use of order statistics or other computationally expensive procedures, the proposed denoising algorithm is efficiently implemented using convolution blocks, thereby a significant reduction in computation time is achieved. Moreover, in each stage of the proposed structure, a weighted mean filter of an appropriate kernel size is employed to selectively restore a set of noisy pixels qualified by a reliability criterion to improve the performance. The simulation results show that the proposed method denoises much faster than all its competent counterparts, while it achieves a significant performance in both quantitative criteria and visual effects. While noise removal by traditional methods such as AMF takes about 1.092 s and by fast state-of-the-art methods such as NAHAT takes about 0.065 s on each image of the BSDS500 dataset on average, the proposed method dramatically reduces the execution time to 0.005 s.
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Notes
As we shall see later on, this simplifies the processing in subsequent blocks.
There should remain no unrestored noisy pixels at the output of the last stage. Therefore, \(\mathbf {Y_3}\) is an all 1 matrix for the last stage (or, equivalently, the rightmost branch in Fig. 2 is omitted in the last stage).
References
Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898–916. https://doi.org/10.1109/TPAMI.2010.161
Astola J, Kuosmanen P (1997) Fundamentals of Nonlinear Digital Filtering, vol 8. CRC Press, Boca Raton
Asuni N, Giachetti A (2014) TESTIMAGES: a Large-scale Archive for Testing Visual Devices and Basic Image Processing Algorithms. In: Giachetti A (ed) Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference, The Eurographics Association, https://doi.org/10.2312/stag.20141242
Bhadouria VS, Ghoshal D, Siddiqi AH (2014) A new approach for high density saturated impulse noise removal using decision-based coupled window median filter. SIViP 8(1):71–84. https://doi.org/10.1007/s11760-013-0487-5
Bovik AC (2005) Handbook of Image and Video Processing, 2nd edn. Communications, Networking and Multimedia, Academic Press Inc, https://doi.org/10.1016/B978-0-12-119792-6.X5062-1
Bovik A (1987) Streaking in median filtered images. IEEE Trans Acoust Speech Signal Process 35(4):493–503. https://doi.org/10.1109/TASSP.1987.1165153
Chan RH, Ho C-W, Nikolova M (2005) Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization. IEEE Trans Image Process 14(10):1479–1485. https://doi.org/10.1109/TIP.2005.852196
Chen F, Huang M, Ma Z, Li Y, Huang Q (2020) An iterative weighted-mean filter for removal of high-density salt-and-pepper noise. Symmetry 12(12), https://doi.org/10.3390/sym12121990
Chen Q, Wan Y (2014) A new framework for image impulse noise removal with postprocessing. In: 2014 IEEE Visual Communications and Image Processing Conference, pp 442–445, https://doi.org/10.1109/VCIP.2014.7051601
Enginoğlu S, Erkan U, Memiş S (2019) Pixel similarity-based adaptive riesz mean filter for salt-and-pepper noise removal. Multimed Tools Appl 78(24):35401–35418. https://doi.org/10.1007/s11042-019-08110-1
Enginoğlu S, Erkan U, Memiş S (2020) Adaptive cesáro mean filter for salt-and-pepper noise removal. El-Cezeri J Sci Eng 7(1):304–314. https://doi.org/10.31202/ecjse.646359
Erkan U, Gökrem L (2018) A new method based on pixel density in salt and pepper noise removal. Turk J Electr Eng Comput Sci 26(1):162–171. https://doi.org/10.3906/elk-1705-256
Erkan U, Gökrem L, Enginoglu S (2018) Different applied median filter in salt and pepper noise. Comput Electr Eng 70:789–798. https://doi.org/10.1016/j.compeleceng.2018.01.019
Erkan U, Thanh DNH, Hieu LM, Engínoğlu S (2019) An iterative mean filter for image denoising. IEEE Access 7:167847–167859. https://doi.org/10.1109/ACCESS.2019.2953924
Erkan U, Enginoğlu S, Thanh DN et al (2020) Adaptive frequency median filter for the salt and pepper denoising problem. IET Image Proc 14(7):1291–1302. https://doi.org/10.1049/iet-ipr.2019.0398
Erkan U, Thanh DN, Enginoğlu S, Memiş S (2020b) Improved adaptive weighted mean filter for salt-and-pepper noise removal. In: 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), pp 1–5, https://doi.org/10.1109/ICECCE49384.2020.9179351
Esakkirajan S, Veerakumar T, Subramanyam AN, PremChand CH (2011) Removal of high density salt and pepper noise through modified decision based unsymmetric trimmed median filter. IEEE Signal Process Lett 18(5):287–290. https://doi.org/10.1109/LSP.2011.2122333
Fareed SBS, Khader SS (2018) Fast adaptive and selective mean filter for the removal of high-density salt and pepper noise. IET Image Proc 12(8):1378–1387. https://doi.org/10.1049/iet-ipr.2017.0199
Garg B, Arya KV (2020) Four stage median-average filter for healing high density salt and pepper noise corrupted images. Multimed Tools Appl 79(43):32305–32329. https://doi.org/10.1007/s11042-020-09557-3
Ghimpeteanu G, Batard T, Bertalmío M, Levine S (2016) A decomposition framework for image denoising algorithms. IEEE Trans Image Process 25(1):388–399. https://doi.org/10.1109/TIP.2015.2498413
Gonzalez RC, Woods RE (2018) Digital Image Processing, 4th edn. Pearson Education
Hemanth J, Balas VE (2019) Nature inspired optimization techniques for image processing applications. Springer International Publishing, https://doi.org/10.1007/978-3-319-96002-91
Horé A, Ziou D (2010) Image quality metrics: Psnr vs. ssim. In: 20th International Conference on Pattern Recognition, pp 2366–2369, https://doi.org/10.1109/ICPR.2010.579
Hwang H, Haddad RA (1995) Adaptive median filters: new algorithms and results. IEEE Trans Image Process 4(4):499–502. https://doi.org/10.1109/83.370679
Jayaraj V, Ebenezer D (2010) A new switching-based median filtering scheme and algorithm for removal of high-density salt and pepper noise in images. EURASIP J Adv Signal Process p 690218, https://doi.org/10.1155/2010/690218
Karthik B, Krishna Kumar T, Vijayaragavan SP, Sriram M (2021) Removal of high density salt and pepper noise in color image through modified cascaded filter. J Ambient Intell Humaniz Comput 12(3):3901–3908. https://doi.org/10.1007/s12652-020-01737-1
Memiş S, Erkan U (2021) Different adaptive modified riesz mean filter for high-density salt-and-pepper noise removal in grayscale images. Avrupa Bilim ve Teknoloji Dergisi pp 359 – 367, https://doi.org/10.31590/ejosat.873312
Srinivasan KS, Ebenezer D (2007) A new fast and efficient decision-based algorithm for removal of high-density impulse noises. IEEE Signal Process Lett 14(3):189–192. https://doi.org/10.1109/LSP.2006.884018
Thanh DN, Hien NN, Kalavathi P, Prasath VS (2020a) Adaptive switching weight mean filter for salt and pepper image denoising. Procedia Computer Science 171:292–301, https://doi.org/10.1016/j.procs.2020.04.031, third International Conference on Computing and Network Communications (CoCoNet’19)
Thanh DNH, Hai NH, Prasath VBS, Hieu LM, Tavares JMRS (2020) A two-stage filter for high density salt and pepper denoising. Multimed Tools Appl 79(29):21013–21035. https://doi.org/10.1007/s11042-020-08887-6
Thanh DNH, Thanh LT, Hien NN, Prasath S (2020) Adaptive total variation l1 regularization for salt and pepper image denoising. Optik 208:163677. https://doi.org/10.1016/j.ijleo.2019.163677
Thanh DN, Prasath V, Phung TK, Hung NQ (2021) Impulse denoising based on noise accumulation and harmonic analysis techniques. Optik 241:166163. https://doi.org/10.1016/j.ijleo.2020.166163
Varatharajan R, Vasanth K, Gunasekaran M, Priyan M, Gao X (2018) An adaptive decision based kriging interpolation algorithm for the removal of high density salt and pepper noise in images. Comput Electr Eng 70:447–461. https://doi.org/10.1016/j.compeleceng.2017.05.035
Vasanth K, Varatharajan R (2020) An adaptive content based closer proximity pixel replacement algorithm for high density salt and pepper noise removal in images. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02376-2
Wan Y, Chen Q (2010) A novel quadratic type variational method for efficient salt-and-pepper noise removal. In: 2010 IEEE International Conference on Multimedia and Expo, pp 1055–1060, https://doi.org/10.1109/ICME.2010.5583306
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612. https://doi.org/10.1109/TIP.2003.819861
Xu S, Yang X, Jiang S (2017) A fast nonlocally centralized sparse representation algorithm for image denoising. Signal Process 131:99–112, https://doi.org/10.1016/j.sigpro.2016.08.006
Zhang P, Li F (2014) A new adaptive weighted mean filter for removing salt-and-pepper noise. IEEE Signal Process Lett 21(10):1280–1283. https://doi.org/10.1109/LSP.2014.2333012
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Rafiee, A.A., Farhang, M. A very fast and efficient multistage selective convolution filter for removal of salt and pepper noise. J Ambient Intell Human Comput 14, 1–17 (2023). https://doi.org/10.1007/s12652-022-03747-7
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DOI: https://doi.org/10.1007/s12652-022-03747-7