An Effective Algorithm for Denoising Salt-And-Pepper Noise in Real-Time

Main Article Content

Obed Appiah
James Benjamin Hayfron-Acquah
Michael Asante

Abstract

For computer vision systems to effectively perform diagnoses, identification, tracking, monitoring and surveillance, image data must be devoid of noise. Various types of noises such as Salt-and-pepper or Impulse, Gaussian, Shot, Quantization, Anisotropic, and Periodic noises corrupts images making it difficult to extract relevant information from them. This has led to a lot of proposed algorithms to help fix the problem. Among the proposed algorithms, the median filter has been successful in handling salt-and-pepper noise and preserving edges in images. However, its moderate to high running time and poor performance when images are corrupted with high densities of noise, has led to various proposed modifications of the median filter. The challenge observed with all these modifications is the trade-off between efficient running time and quality of denoised images. This paper proposes an algorithm that delivers quality denoised images in low running time. Two state-of-the-art algorithms are combined into one and a technique called Mid-Value-Decision-Median introduced into the proposed algorithm to deliver high quality denoised images in real-time. The proposed algorithm, High-Performance Modified Decision Based Median Filter (HPMDBMF) runs about 200 times faster than the state-of-the-art Modified Decision Based Median Filter (MDBMF) and still generate equivalent output.

Keywords:
Image denoising, median filter, decision based median filters, real-time image processing, real-time computer vision

Article Details

How to Cite
Appiah, O., Hayfron-Acquah, J. B., & Asante, M. (2020). An Effective Algorithm for Denoising Salt-And-Pepper Noise in Real-Time. Asian Journal of Research in Computer Science, 6(1), 14-27. https://doi.org/10.9734/ajrcos/2020/v6i130149
Section
Original Research Article

References

Goyal B, Dogra A, Agrawal S, Sohi BS, Sharma A. Image denoising review: From classical to state-of-the-art approaches. Information Fusion; 2020.
DOI:org/10.1016/j.inffus.2019.09.003

Buades A, Coll B, Morel JM. A review of image denoising algorithms, with a new one. Multiscale Modeling and Simulation; 2005.
DOI:https://doi.org/10.1137/040616024

Oliva D, Abd-Elaziz M, Hinojosa S. Image Processing. Studies in Computational Intelligence. 2019;27–45.
DOI:10.1007/978-3-030-12931-6_4

Szeliski R. Computer Vision: Algorithms and Applications. Texts in Computer Science. Springer, London.
DOI:https://doi.org/10.1007/978-1-84882-935-0_1

Asano A, Itoh K, Ichioka Y. Optimization of the weighted median filter by learning. Optics Express. 1991;16(3):168–170.

Huber PJ. Robust statistics. 1st Ed. New York: Wiley; 1981.

Hampel FR, Ronchetti EM, Rousseeuw PJ, Stahel WA. Robust Statistics: The Approach Based on Influence Functions. 1st ed. Wiley: New Jersey; 2005.
DOI:https://doi.org/10.1002/9781118186435

Stewart CV. Robust parameter estimation in computer vision. SIAM Review. 1999; 41(3):513–537. DOI:https://doi.org/10.1137/s0036144598345802

Tukey JW. Exploratory data analysis. 1st ed. Addison-Wesley. Reading, MA; 1977.

Pitas I, Venetsanopoulos AN. Order statistics in digital image processing. Proc. IEEE. 1992;80(12):1893–1921.

Arce GR, Paredes JL. Recursive weighted median filters admitting negative weights and their optimization. IEEE Transactions on Signal Processing. 2000;48(3):768–779.

Forouzan AR, Araabi BN. Iterative median filtering for restoration of images with impulsive noise. Proceedings of the IEEE International Conference on Electronics, Circuits, and Systems. 2003;1(l):232–235.
DOI:https://doi.org/10.1109/ICECS.2003.1302019

Liu L, Chen CLP, Zhou Y, You X. A new weighted mean filter with a two-phase detector for removing impulse noise. Information Sciences. 2015;315:1–16.

Paeth AW. Median finding on a 3 × 3 grid. Graphics Gems; 1990.
DOI:https://doi.org/10.1016/b9780080507538.500449

Singh S. An Alternate Algorithm for (3x3) Median Filtering of Digital Images. International Journal of Computers & Technology. 2011;1(1):7–9.
DOI:https://doi.org/10.24297/ijct.v1i1.6732

Huang TS, Yang GJ, Tang GY. A Fast Two Dimensional Median Filtering Algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing. 1979;27(1):13–18.
DOI:https://doi.org/10.1109/TASSP.1979.1163188

Weiss B. Fast median and bilateral filtering. ACM Transactions on Graphics; 2006, DOI:https://doi.org/10.1145/1141911.1141918

Perreault S, Hebert P. Median Filtering in Constant Time. IEEE Transactions on Image Processing. 2007;16(9):2389–2394. DOI:https://doi.org/10.1109/TIP.2007.902329

Zhu Y, Huang C. An Improved Median Filtering Algorithm for Image Noise Reduction. Physics Procedia. 2012;25: 609–616. DOI:https://doi.org/10.1016/j.phpro.2012.03.133

Zhang Q, Xu L, Jia J. 100+ times faster weighted median filter (WMF). Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2014;2830–2837.
DOI:https://doi.org/10.1109/CVPR.2014.362

Narendra PM. A separable median filter for image noise smoothing. Pattern Analysis and Machine Intelligence, IEEE Transactions on, PAMI. 1981;3(1):20 29.

Lu Y, Jiang L, Dai M, Li S. Sort optimization algorithm of median filtering based on FPGA. In 2010 International Conference on Machine Vision and Human Machine Interface, MVHI; 2010.
DOI:https://doi.org/10.1109/MVHI.2010.145

Appiah O, Asante M, Hayfron-Acquah JB Adaptive approximated median filtering algorithm for impulse noise reduction. Asian Journal of Mathematics and Computer Research. 2016;12(2):134144.

Appiah O, Hayfron-Acquah BJ, Asante M. Approximation techniques for reducing running time of image pre-processing algorithms [Ph. D. Thesis]. Department of Computer Science, Kwame Nkrumah University of Science and Technology, unpublished.

Marcus RC, Ward WC. DP : A Fast Median Filter Approximation. Los Alamos National Lab.(LANL). Los Alamos-NM (United States) 2013;1–11.
Available:https://permalink.lanl.gov/object/tr?what=info:lanl-repo/lareport/LA-UR-13-25331

Kunsoth R, Biswas M. Modified decision based median filter for impulse noise removal. 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai. 2016;1316-1319.

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

Ko J, Lee YH. Center weighted median filters and their applications to image enhancement, IEEE Trans. Circuits Syst. 1991;38(9):984–993.