Warm Start of Multi-channel Weighted Nuclear Norm Minimization for Color Image Denoising

摘要

The multi-channel weighted nuclear norm mini-mization (MCWNNM) performs excellently on color image de-noising. Can we further improve the effectiveness of MCWNNM? To answer this question, we propose warm start of multi-channel weighted nuclear norm minimization (WS-MCWNNM) for color image denoising. In WS-MCWNNM, spatial filtering is utilized to preprocess the noisy image, and the result of preprocessing is treated as the input of MCWNNM model to remove noise. Ex-periment results show the proposed WS-MCWNNM outperforms MCWNNM both on peak signal-to-noise ratio and structural similarity.