Abstract
By extraction of the thoughts of non-linear model and adaptive model match, an improved Nagao filter is brought. Meanwhile a technique based on simplified pulse coupled neural network and used for noise positioning, is put forward. Combining the two methods above, we acquire a new method that can restore images corrupted by salt and pepper noise. Experiments show that this method is more preferable than other popular ones, and still works well while noise density fluctuates severely.
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References
Davis L S, Rosenfeld A. Noise cleaning by iterated local averaging. IEEE Trans Syst, Man and Cybernetics, 1978, (7): 705–710
Nodes T A, Gallagher N C Jr. Median filters: some modifications and their properties. IEEE Trans Acoust, Speech, Sig Proc, 1982, 30(5): 739–746
Brownrigg D R K. The weighted median filter. Commun Associat Comput Mach, 1984, 27(8): 807–818
Ko S J, Lee Y H. Center weighted median filters and their applications to image enhancement. IEEE Trans Circ Syst, 1991, 38(9): 984–993
Agaian S, Choi D S, Noonan J. Image compression using fuzzy subband decomposition. In: Yeneds J, ed. FUZZ-IEEE 2000-95th IEEE International Conference on Fuzzy Syst, San Antonio, TX, USA: Institute of Electrical and Electronics Engineers Inc, 2004. 894–899
Senel H G, Alan P II R, Benoit D. Topological median filters. IEEE Trans Image Proc, 2002, 11(2): 89–104
Sun T, Neuvo Y. Detail-preserving median based filter in image processing. Patt Recog Lett, 1994, 15(4): 341–347
Yuan S Q, Tan Y H. A median-subset-type adaptive median filter. J Imag Graph, 2007, 12(4): 608–612
Ranganath H S. Object detection using pulse coupled neural networks. IEEE Trans Neural Networks, 1999, 10(3): 615–620
Makoto N, Takashi M. Edge preserving smoothing. Comput Graph Imag Proc, 1979, (9): 394–407e
Zhu J H, Yang X, Li J, et al. Texture analysis based detail preserving smoothing filter. J Imag Graph, 2001, 6(11): 1058–1064
Eckhorn R, Reitboeck H H, Arndt M, et al. Feature linking via synchronization among distributed assemblies: simulation of results from Cat Cortex. Neural Comput, 1990, 2(3): 293–307
Gu X D, Guo S D, Yu D H. A new approach for noise reducing of image based on PCNN. J Electr Info Tech, 2002, 24(10): 1304–1309
Li Y G, Shi M H, Wei Y W. Image Gauss noise filtering based on PCNN. Comput Engin Appl, 2007, 43(1): 65–68
Shi M H, Mao J H, Liang Y. Method for filtering image contaminated with strong Gaussian noise. Comput Appl, 2007, 27(7): 637–640
Wang W, Li M, Liu G H. New color image filtering algorithm based on PCNN. Comput Engin Design, 2007, 28(14): 3413–3415
Ma Y D, Shi F, Li L. Gaussian noise filter based on PCNN. IEEE ICNNSP, 2003
Zhang J Y, Lu Z J, Shi L, et al. Filtering images contaminated with pep and salt type noise with pulse-coupled networks. Sci China Ser F-Inf Sci, 2005, 48(3): 322–334
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Supported by the National Technical Innovation Project Essential Project Cultivate Project (Grant No. 706928), the Natural Science Fund of Jiangsu Province (Grant No. BK2007103)
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Zhang, Y., Wu, L. Improved image filter based on SPCNN. Sci. China Ser. F-Inf. Sci. 51, 2115–2125 (2008). https://doi.org/10.1007/s11432-008-0124-z
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DOI: https://doi.org/10.1007/s11432-008-0124-z