  Group sparsity has shown great potential in various low-level vision tasks
(e.g, image denoising, deblurring and inpainting). In this paper, we propose a
new prior model for image denoising via group sparsity residual constraint
(GSRC). To enhance the performance of group sparse-based image denoising, the
concept of group sparsity residual is proposed, and thus, the problem of image
denoising is translated into one that reduces the group sparsity residual. To
reduce the residual, we first obtain some good estimation of the group sparse
coefficients of the original image by the first-pass estimation of noisy image,
and then centralize the group sparse coefficients of noisy image to the
estimation. Experimental results have demonstrated that the proposed method not
only outperforms many state-of-the-art denoising methods such as BM3D and WNNM,
but results in a faster speed.
