A wavelet-based image denoising using least squares support vector machine

The least squares support vector machine (LS-SVM) is a modified version of SVM, which uses the equality constraints to replace the original convex quadratic programming problem. Consequently, the global minimizer is much easier to obtain in LS-SVM by solving the set of linear equation. LS-SVM has shown to exhibit excellent classification performance in many applications. In this paper, a wavelet-based image denoising using LS-SVM is proposed. Firstly, the noisy image is decomposed into different subbands of frequency and orientation responses using the wavelet transform. Secondly, the feature vector for a pixel in a noisy image is formed by the spatial regularity in wavelet domain, and the LS-SVM model is obtained by training. Then the wavelet coefficients are divided into two classes (noisy coefficients and noise-free ones) by LS-SVM training model. Finally, all noisy wavelet coefficients are relatively well denoised by soft-thresholding method. Extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than those state-of-the-art denoising techniques. Especially, the proposed method can preserve edges very well while removing noise.