Image Denoising Based on Wavelet Support Vector Machine

Authors:
Shaoming Zhang;Ying Chen
Affiliations:
The Research Center of Remote Sensing and Space Information Technology, Tongji University, Shanghai, 200092, China;The Research Center of Remote Sensing and Space Information Technology, Tongji University, Shanghai, 200092, China
Venue:
Computational Intelligence and Security
Year:
2007

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Abstract

In this paper, a new image denoising method based on wavelet analysis and support vector machine regression (SVR) is presented. The feasibility of image denoising via support vector regression is discussed and is demonstrated by an illustrative example which denoise a 1-dimension signal with Gauss KBF SVM. The wavelet theory is discussed and applied to construct the wavelet kernel, then the wavelet support vector machine (WSVM) is proposed. The result of experiment shows that the denoising method based on WSVM can reduce noise well, the comparison between the method proposed in this paper and other ones is also given which proves this method is better than Gaussian KBF SVM and other traditional methods.