Group Sparsity Residual with Non-Local Samples for Image Denoising
This work addresses image denoising for computer vision applications, offering an incremental improvement over existing GSR methods.
The paper tackled the challenge of estimating residuals in group sparsity residual (GSR) for image denoising by proposing GSR-NLS, which uses non-local samples as reference, resulting in improved performance and competitive speed compared to state-of-the-art methods.
Inspired by group-based sparse coding, recently proposed group sparsity residual (GSR) scheme has demonstrated superior performance in image processing. However, one challenge in GSR is to estimate the residual by using a proper reference of the group-based sparse coding (GSC), which is desired to be as close to the truth as possible. Previous researches utilized the estimations from other algorithms (i.e., GMM or BM3D), which are either not accurate or too slow. In this paper, we propose to use the Non-Local Samples (NLS) as reference in the GSR regime for image denoising, thus termed GSR-NLS. More specifically, we first obtain a good estimation of the group sparse coefficients by the image nonlocal self-similarity, and then solve the GSR model by an effective iterative shrinkage algorithm. Experimental results demonstrate that the proposed GSR-NLS not only outperforms many state-of-the-art methods, but also delivers the competitive advantage of speed.