A new low-rank sparse image denoising algorithm based on non-local self-similarity

Abstract

Aiming at the existing problems of denoising methods based low-rank and sparse representation that easily lose image details, resulting in low image denoising quality, we propose an image denoising method based on low-rank and sparse representations in a non-local framework. The proposed algorithm consists of two steps: firstly, similar image blocks are matched and grouped, a low-rank matrix recovery model is established, and then a random matrix theory is used to implement preliminary denoising; secondly, the artifacts in the image are removed by non-local sparse representation, and the noise atoms are clipped. Theoretical analysis and experimental results show that the proposed method can filter out noise better, retain image detail information, and obtain better image visual effects compared with the current popular denoising methods of the same kind.