2016/09/12 by Zhiyuan Zha, Zha, Zhiyuan, Xin Liu +17 · 3 citations
Computer Science · Engineering · Mathematics · #Advanced Image Fusion Techniques #Algorithm #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Constraint (computer-aided design) #Deblurring #FOS: Computer and information sciences #Image (mathematics) #Image and Signal Denoising Methods #Image denoising #Image processing #Image restoration #Inpainting #Mathematics #Noise reduction #Non-local means #Pattern recognition (psychology) #Residual #Sparse and Compressive Sensing Techniques #cs.CV
paper · pdf · doi:10.48550/arxiv.1609.03302
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2016/09/12 · arxiv created 2017/03/03 · arxiv updated 2017/03/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.