2017/03/01 by Zhiyuan Zha, Zha, Zhiyuan, Xinggan Zhang +7 · 9 citations
Computer Science · Engineering · Mathematics · #Advanced Image Fusion Techniques #Algorithm #Artificial intelligence #Bayesian probability #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Constraint (computer-aided design) #FOS: Computer and information sciences #Image (mathematics) #Image and Signal Denoising Methods #Mathematics #Noise (video) #Noise reduction #Pattern recognition (psychology) #Prior probability #Residual #Sparse and Compressive Sensing Techniques #Sparse approximation #cs.CV
paper · pdf · doi:10.48550/arxiv.1703.00297
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2017/03/01 · arxiv created 2017/07/31 · arxiv updated 2017/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Group-based sparse representation has shown great potential in image denoising. However, most existing methods only consider the nonlocal self-similarity (NSS) prior of noisy input image. That is, the similar patches are collected only from degraded input, which makes the quality of image denoising largely depend on the input itself. However, such methods often suffer from a common drawback that the denoising performance may degrade quickly with increasing noise levels. In this paper we propose a new prior model, called group sparsity residual constraint (GSRC). Unlike the conventional group-based sparse representation denoising methods, two kinds of prior, namely, the NSS priors of noisy and pre-filtered images, are used in GSRC. In particular, we integrate these two NSS priors through the mechanism of sparsity residual, and thus, the task of image denoising is converted to the problem of reducing the group sparsity residual. To this end, we first obtain a good estimation of the group sparse coefficients of the original image by pre-filtering, and then the group sparse coefficients of the noisy image are used to approximate this estimation. To improve the accuracy of the nonlocal similar patch selection, an adaptive patch search scheme is designed. Furthermore, to fuse these two NSS prior better, an effective iterative shrinkage algorithm is developed to solve the proposed GSRC model. Experimental results demonstrate that the proposed GSRC modeling outperforms many state-of-the-art denoising methods in terms of the objective and the perceptual metrics.