Abstract:

For the existing problems of the K-sigular value decomposition (SVD） denoising method, a new denoising method based on non-local regularized sparse representation is proposed, which combines the structural clustering and dictionary learning. Firstly, image blocks that are similar in structure are clustered by using the idea of non-local denoising. It reinforces the adaptive ability of dictionary because each image block runs dictionary learning independently. Then, structured dictionaries within classes are learned through substituting the K-SVD by sparse K-SVD. Finally, in order to improve the effect of image reconstruction, the sparse coefficient error regularization is introduced to revise the sparse coefficient. Compared with the traditional K-SVD denoising algorithm, experiments show that the proposed method can protect the information of image structure effectively and promote the result of denoising greatly. Simultaneously, without decreasing the structural similarity image measurement value, the peak signal to noise ratio value is very close and even better than the advanced denoising algorithm.