Abstract
Stack filters are often employed for suppressing the pulse noise. In general, the larger sizes the stack filters are, the better results are. Unfortunately, available algorithms for designing stack filters can only be suit for small window sizes due to their huge computational complexities. This paper presents a new fast adaptive algorithm for designing a stack filter with large windows. The idea of the new algorithm is to divide a lager window into many sub-windows. The procedures of dividing a large window are given. An Immune Memory Clonal Selection Algorithm is employed to design the stack filters with small window sizes. Because of its highly parallel structure, it can be very fast implemented. As an experiment, the algorithm was used to restore images corrupted by uncorrelated additive noise with the level from 10% to 50 %. The results show that the algorithm is effective and feasible.
Nation Natural Science Fund of China Under Grant No. 60372047.
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References
Wendt, P.D., Coyle, E.J., Gallagher, N.C.: Stack filters. IEEE Trans. Acoust., Speech, Signal Processing ASSP-34, 898–911 (1986)
Fitch, J.P., Colyle, E.J., Gallagher, N.C.: Median filtering by threshold decomposition. IEEE Trans. Acoust., Speech, Signal Processing ASSP-32, 1183–1188 (1984)
Yin, L., Yang, R., Gabbouj, M., Neuvo, Y.: Weighted median filters: A tutorial. IEEE Trans. Circuits Syst. II 43, 157–192 (1996)
Tukey, J.W.: Nonlinear (non-superposable) methods for smoothing data. In: EASCON Conf. Rec. (1974)
Nodes, T., Gallagher, N.C.: Median filters: Some modifications and their properties. IEEE Trans. Acoust., Speech, Signal Processing ASSP-30, 739–746 (1982)
Maragos, P.A., Schafer, R.W.: A unification of linear, median, order statistics, and morphological filters under mathematical morphology. In: Proc. 1985 Int. Conf. Acoust., Speech, Signal Processing, Tampa, FL (March 1985)
Adams III, G., Coyle, E.J., Lin, L., Lucke, L., Parhi, K.: Input compression and efficient VLSI architectures for rank order and stack filters. IEEE Trans. Acoust., Speech, Signal Processing 38, 441–453 (1994)
Yoo, J., Bouman, C.A., Delp, E.J., Coyle, E.J.: The nonlinear prefiltering and difference of estimates approaches to edge detection: Application of stack filters. Graph. Models Image Process. 55, 140–159 (1993)
Tabus, I., Petrescu, D., Gabbouj, M.: A training framework for stack and Boolean filtering – Fast optimal design procedures and robustness case study. IEEE Trans. Image Processing 5, 809–826 (1996)
Yoo, J., Fong, K.L., Huang, J.J., Coyle, E.J., Adams, G.B.: A fast algorithm for designing stack filters. IEEE Trans. Image Processing 8, 1014–1028 (1999)
Dong, W.S., Shi, G.M., Zhang, L.: Immune memory clonal selection algorithm for designing stack filters. Neurocomputing (accepted)
Muroga, S.: Threshold logic and its applications. Wiley, New York (1971)
Woolfries, N., et al.: Non Linear Image Processing on Field Programmable Gate Arrays. In: NOBLESSE Workshop on Non-linear Model Based Image Analysis. Proc. NMBIA 1998, Glasgow, pp. 301–307 (1998)
De Castro, L.N., Von Zuben, F.J.: The clonal selection algorithm with engineering applications. In: Proceedings of Genetic and Evolutionary Computation Conference, Workshop on Artificial Immune Systems and Their Applications, pp. 36–37 (July 2000)
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Shi, G., Dong, W., Zhang, L., Pan, J. (2006). A New Fast Algorithm for Training Large Window Stack Filters. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_91
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DOI: https://doi.org/10.1007/11881223_91
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