Group Sparse Coding for Image Denoising
This addresses image denoising for applications like photography or medical imaging, but it is incremental as it builds on an existing method.
The paper tackled the problem of image denoising by adapting group sparse representation, which had unstable results, and proposed a progressive algorithm that achieved superior performance compared to state-of-the-art methods.
Group sparse representation has shown promising results in image debulrring and image inpainting in GSR [3] , the main reason that lead to the success is by exploiting Sparsity and Nonlocal self-similarity (NSS) between patches on natural images, and solve a regularized optimization problem. However, directly adapting GSR[3] in image denoising yield very unstable and non-satisfactory results, to overcome these issues, this paper proposes a progressive image denoising algorithm that successfully adapt GSR [3] model and experiments shows the superior performance than some of the state-of-the-art methods.