文献类型：期刊文献

英文题名：Non-local sparse regularization model with application to image denoising

作者：He, Ning[1];Wang, Jin-Bao[1];Zhang, Lu-Lu[1];Xu, Guang-Mei[1];Lu, Ke[2]

第一作者：何宁

通讯作者：He, N[1]

机构：[1]Beijing Union Univ, Coll Informat Technol, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China;[2]Univ Chinese Acad Sci, Beijing 100049, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室|北京联合大学智慧城市学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Informat Technol, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China.|[1141734]北京联合大学智慧城市学院;[11417]北京联合大学;[11417103]北京联合大学北京市信息服务工程重点实验室;

年份：2016

卷号：75

期号：5

起止页码：2579-2594

外文期刊名：MULTIMEDIA TOOLS AND APPLICATIONS

收录：;EI(收录号：20150700533536);Scopus(收录号：2-s2.0-84959921690);WOS:【SCI-EXPANDED(收录号:WOS:000372027000011)】；

基金：This work was supported by the National Natural Science Foundation of China (Grant Nos. 61370138, 61271435, 61103130, U1301251), National Program on Key Basic Research Projects (973 programs) (Grant Nos. 2010CB731804-1, 2011CB706901-4), Project of Construction of Innovative Teams and Teacher Career Development for Universities and Colleges under Beijing Municipality (Grant Nos. IDHT20130513, CIT&TCD20130513), Beijing Municipal Natural Science Foundation (Grant No. 4141003), and Beijing Municipal Party Committee Organization Department of Outstanding Talent Project (Grant No. 2010D005022000011).

语种：英文

外文关键词：Image denoising; Non-local means; Sparse coding; Regularization; Self-similarity

摘要：We study problems related to denoising of natural images corrupted by Gaussian white noise. Important structures in natural images such as edges and textures are jointly characterized by local variation and nonlocal invariance. Both provide valuable schemes in the regularization of image denoising. In this paper, we propose a framework to explore two sets of ideas involving on the one hand, locally learning a dictionary and estimating the sparse regularization signal descriptions for each coefficient; and on the other hand, nonlocally enforcing the invariance constraint by introducing patch self-similarities of natural images into the cost functional. The minimization of this new cost functional leads to an iterative thresholding-based image denoising algorithm; its efficient implementation is discussed. Experimental results from image denoising tasks of synthetic and real noisy images show that the proposed method outperforms the state-of-the-art, making it possible to effectively restore raw images from digital cameras at a reasonable speed and memory cost.