Image denoising using SVM classification in nonsubsampled contourlet transform domain

For image denoising, the main challenge is how to preserve the information-bearing structures such as edges and textures to get satisfactory visual quality when improving the signal-to-noise-ratio (SNR). Edge-preserving image denoising has become a very intensive research topic. In this paper, we propose an image denoising using support vector machine (SVM) classification in nonsubsampled contourlet transform (NSCT) domain. Firstly, the noisy image is decomposed into different subbands of frequency and orientation responses using the NSCT. Secondly, the feature vector for a pixel in a noisy image is formed by the spatial regularity in NSCT domain, and the least squares support vector machine (LS-M) model is obtained by training. Then the NSCT detail coefficients are divided into two classes (edge-related coefficients and noise-related ones) by LS-SVM training model. Finally, the detail subbands of NSCT coefficients are denoised by using shrink method, in which the adaptive Bayesian threshold is utilized. 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.