Abstract: The traditional image denoising algorithm based on low-rank matrix recovery only has the low rank restraint,and when Gaussian noise is too large,it will lead to insufficient denoise or serious loss of detail.To overcome the disadvantages of the image denoising algorithm based on low-rank matrix recovery,a novel robust image denoising algorithm was proposed,which adds Gaussian restraint into the low rank restraint model.Inspired by reweighted L1 minimization for sparsity enhancement,reweighting singular values were used to enhance low rank of a matrix,and an efficient iterative reweighting scheme was proposed for enhancing low rank and sparsity simultaneously.Finally,to verify the denoi-sing capability of the presented approach,images with different noise types and simulation parameters were generated using the presented method and the results were compared with the traditional image denoising algorithm based on low-rank matrix recovery.Performance analysis of peak signal to noise ratio and structural similarity index were carried on at the same time.The experimental results show that the mixture noise image denoising using reweighted low-rank matrix recovery algorithm can enhance low rank and sparsity of a matrix simultaneously,guarantee visual effect and keep the details,at the same time,the objective evaluation indexes are improved.

Key words: Image denoising,Low-rank matrix recovery,Reweighted,Sparse