关键词: 图像去噪, 低秩表示（LRR）, TV范数, 非凸惩罚函数, 非局部自相似性

Abstract: Since nuclear norm tends to underestimate the nonzero singular values in Low-Rank Representation model（LRR）, a parameterized nonconvex penalty function is employed to estimate nonzero singular values. Meanwhile using the ability of Total Variation（TV） norm to preserve image edge information and enhance the smoothness of image region, a new image denoising algorithm is proposed by applying the TV norm constraint to the coefficient matrix in LRR model, and the corresponding model is solved by an alternating minimization method. Exploiting the non-local self-similarity prior inherent in the image, the proposed algorithm can effectively find and remove noise, while enhancing the structure and area smoothness, which improves the quality of the restored image. Experimental results indicate that the proposed algorithm achieves a competitive denoising performance, especially for high-intensity noise, in comparison with state-of-the-art algorithms in terms of objective evaluation and visual effect.

Key words: image denoising, Low?Rank Representation（LRR）, Total Variation（TV） norm, nonconvex penalty function, non-local self-similarity