Application of SVM-based filter using LMS learning algorithm for image denoising

Authors:
Tzu-Chao Lin;Chien-Ting Yeh;Mu-Kun Liu
Affiliations:
Department of Computer Science and Information Engineering, WuFeng University, Chiayi, Taiwan, R.O.C;Department of Computer Science and Information Engineering, WuFeng University, Chiayi, Taiwan, R.O.C;Department of Computer Science and Information Engineering, WuFeng University, Chiayi, Taiwan, R.O.C
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Year:
2010

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Abstract

In this paper, a novel adaptive filter based on support vector machines (SVMs) that preserves image details and effectively suppresses impulsive noise is proposed. The filter employs an SVM impulse detector to judge whether an input pixel is noisy. If a noisy pixel is detected, a median filter is triggered to replace it. Otherwise, it stays unchanged. To improve the quality of the restored image, an adaptive LUM filter based on scalar quantization (SQ) is activated. The optimal weights of the adaptive LUM filter are obtained using the least mean square (LMS) learning algorithm. Experimental results demonstrate that the proposed scheme outperforms other decision-based median filters in terms of noise suppression and detail preservation.