SVM-based Filter Using Evidence Theory and Neural Network for Image Denosing

Tzu-Chao Lin

Department of Computer Science and Information Engineering, WuFeng University, Chiayi 62153, Chinese Taipei.

DOI: 10.4236/jsea.2013.63B023                

Abstract

This paper presents a novel decision-based fuzzy filter  based on support vector machines and Dempster-Shafer  evidence theory for effective noise suppression and detail preservation. The proposed filter uses an SVM impulse detector to judge whether an input pixel is noisy. Sources of evidence are extracted, and then the fusion of evidence based on the evidence theory provides a feature vector that is used as the input data of the proposed SVM impulse detector. A fuzzy filtering mechanism, where the weights are constructed using a counter-propagation neural network, is employed. Experimental results shows that the proposed filter has better performance in terms of noise suppression and detail preservation.

Keywords

Neural Network; Evidence Theory; Impulse Noise; Image Restoration

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	A. M. Mirza, A. Chaudhry, B. Munir, “Spatially adaptive image restoration using fuzzy punctual kriging,” Journal of Computer Science and Technology, vol. 22, pp. 580-589, 2007.
[2]	T. Chen, K. K., Ma, L. H. Chen,“Tri-state median filter for image denoising,” IEEE Trans. Image Processing, vol. 8, pp. 1834-1838, 1999.
[3]	K. Arakawa, “Median filters based on fuzzy rules and its application to image restoration,” Fuzzy Sets and Systems, vol. 77, pp. 3-13, 1996.
[4]	T.-C. Lin, P.-T. Yu, “ Partition fuzzy median filter based on fuzzy rules for image restoration,” Fuzzy Sets and Systems, vol.147, pp. 75-97, 2004.
[5]	T. Sun, Y. Neuvo, “De-tail-preserving median based filters in image processing,” Pattern Recognition Letters, vol. 15,pp. 341-347, 1994.
[6]	K. S. Pankaj, M. Banshidhar, “An improved adaptive impulsive noise suppression scheme for digital images, “International Journal of Electronics and Com-munication, vol. 64, pp. 322-328, 2010.
[7]	T.-C. Lin, P.-T. Yu, “Adaptive two-pass median filter based on support vector machines for image restoration,” Neural Computation, vol. 16, pp. 333-354, 2004.
[8]	H. Liu, F. Sun, Z. Sun, “Image filtering using support vector ma-chine,” Lecture Notes in Computer Science, vol. 3972, pp. 533-538, 2006.
[9]	T.-C. Lin, “Partition belief median filter based on Dempster-Shafer theory in image processing, “Pattern Recognition, vol. 41, pp. 139-151, 2008.
[10]	T.-C. Liu, R.-K. Li, “A new ART-counterpropagation neural network for solving a forecasting problem,” Expert Systems with Applications, vol. 28, pp. 21-27, 2005.
[11]	T.-C. Lin, “A novel deci-sion-based median-type filter using SVM for image de-noising,” International Journal of Innovative Computing, Information and Control, vol. 8, pp. 3189-3202, 2012.