2017/01/03 by Zhiyuan Zha, Zha, Zhiyuan, Xinggan Zhang +7 · 7 citations
Computer Science · Engineering · Mathematics · #Algorithm #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Deblurring #FOS: Computer and information sciences #Image (mathematics) #Image Processing Techniques and Applications #Image and Signal Denoising Methods #Image denoising #Image processing #Image restoration #Mathematics #Noise reduction #Non-local means #Pattern recognition (psychology) #Photoacoustic and Ultrasonic Imaging #Residual #Similarity (geometry) #Sparse approximation #cs.CV
paper · pdf · doi:10.48550/arxiv.1701.00723
published in arXiv (Cornell University) (Cornell University) · arXiv admin note: text overlap with arXiv:1609.03302
arxiv created 2017/01/03 · openalex publication_date 2017/01/03 · arxiv updated 2017/01/04 · openalex created_date 2019/06/27 · openalex updated_date 2026/08/06
Nonlocal image representation has been successfully used in many image-related inverse problems including denoising, deblurring and deblocking. However, a majority of reconstruction methods only exploit the nonlocal self-similarity (NSS) prior of the degraded observation image, it is very challenging to reconstruct the latent clean image. In this paper we propose a novel model for image denoising via group sparsity residual and external NSS prior. To boost the performance of image denoising, the concept of group sparsity residual is proposed, and thus the problem of image denoising is transformed into one that reduces the group sparsity residual. Due to the fact that the groups contain a large amount of NSS information of natural images, we obtain a good estimation of the group sparse coefficients of the original image by the external NSS prior based on Gaussian Mixture model (GMM) learning and the group sparse coefficients of noisy image is used to approximate the estimation. Experimental results have demonstrated that the proposed method not only outperforms many state-of-the-art methods, but also delivers the best qualitative denoising results with finer details and less ringing artifacts.