Support vector regression based image denoising

Support vector regression (SVR) has been applied for blind image deconvolution. In this correspondence, it is applied in the problem of image denoising. After training on noisy images with ground-truth, support vectors (SVs) are identified and their weights are computed. Then the SVs and their weights are used in denoising different images corrupted by random noise at different levels on a pixel-by-pixel basis. The proposed SVR based image denoising algorithm is an example-based approach since it uses SVs in denoising. The SVR denoising is compared with a multiple wavelet domain method (Besov ball projection). Some initial experiments indicate that SVR based image denoising outperforms Besov ball projection method on non-natural images (e.g. document images) in terms of both peak signal-to-noise ratio (PSNR) and visual inspection.