Image denoising via group sparsity residual constraint
This addresses image denoising for computer vision applications, presenting an incremental improvement over existing group sparsity methods.
The paper tackles image denoising by proposing a group sparsity residual constraint (GSRC) prior model, which translates denoising into reducing the group sparsity residual, and results show it outperforms state-of-the-art methods like BM3D and WNNM with faster speed.
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.