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Decision-based filter based on SVM and evidence theory for image noise removal

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Abstract

A decision-based filtering technique is proposed for removing impulsive noise while preserving image details in corrupted images. The proposed filter is based on support vector machines (SVMs), evidence theory, and the least mean square (LMS) learning algorithm. The fusion of evidence based on the Dempster–Shafer evidence theory provides a feature vector that is used as the input data of the proposed SVM impulse detector, which is used to judge whether an input pixel is noisy. If a pixel is detected as noisy, an adaptive CWM filter based on a weight controller is triggered to replace it; otherwise, the pixel stays unchanged. The optimal weights of the adaptive CWM filter are obtained using scalar quantization and the LMS learning algorithm. Experimental results demonstrate that the proposed filter achieves much better performance than the state-of-the-art filters in terms of noise suppression and detail preservation.

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Acknowledgments

The authors would like to thank Prof. Chih-Jen Lin for providing LIBSVM software, a library for support vector machines (version 2.83), and the National Science Council of the Republic of China for financially supporting this research under grant NSC 99-2628-E-274-011.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, WuFeng University, Chiayi, 62153, Taiwan, ROC

    Tzu-Chao Lin

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  1. Tzu-Chao Lin
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Correspondence to Tzu-Chao Lin.

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Lin, TC. Decision-based filter based on SVM and evidence theory for image noise removal. Neural Comput & Applic 21, 695–703 (2012). https://doi.org/10.1007/s00521-011-0648-9

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  • DOI: https://doi.org/10.1007/s00521-011-0648-9

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