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

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DOI: 10.4236/jsea.2013.63B023    2,738 Downloads   4,145 Views   Citations
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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.

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The authors declare no conflicts of interest.

Cite this paper

T. Lin, "SVM-based Filter Using Evidence Theory and Neural Network for Image Denosing," Journal of Software Engineering and Applications, Vol. 6 No. 3B, 2013, pp. 106-110. doi: 10.4236/jsea.2013.63B023.


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