Image Denoising Using Weighted Nuclear Norm Minimization with Preserving Local Structure

Image denoising using weighted nuclear norm minimization (WNNM) is prone to over-smoothing and cannot distinguish intricate and irregular image structures effectively. Image denoising model using relative total variation (RTV) WNNM is proposed. The proposed denoising method, which utilizes the alternate direction multiplier (ADMM) algorithm to solve the corresponding model iteratively, can obtain a clear image. The ADMM algorithm integrates RTV into WNNM and applies the RTV norm constraint to the low-rank representation model of WNNM. Compared to several state-of-the-art denoising methods based on low-rank matrix approximation, the proposed method improves image denoising performance, maintains image edges effectively, and enhances smoothness, particularly for images with high-density noise. Experimental results demonstrate that the proposed method with RTV norm restores image structure effectively and improves denoising performance.