关键词: 加权核范数, 全变分, 图像残差, 二次去噪

Abstract: In order to enhance the visual perception of image denoising, this paper proposes a method named two image denoising method combining Total Variation（TV） with Weighted kernel Norm Minimization（WNNM）. The noisy image is denoised with TV, then the noisy image and the based denoised one are expected to be made a differential operation. After that the result will be filtered with the adaptive Wiener filter. Having been filtered, the image will be overlain with the TV based denoising image, and be made the similar patches collected by using block matching. The final denoised image will be formed after the twice denoising with WNNM. Compared with the original WNNM, Block Matching 3-D （BM3D） and Foveated NL-Means（FNLM）, this method can make a better denoising effect on smooth areas; meanwhile, it also can reduce the spots and false fringe status which are caused by FNLM and BM3D under the high noise. The structure and texture information can be furthest similar as well.

Key words: weighted kernel norm, total variation, image residual, twice denoised