文献类型：期刊文献

英文题名：Multi-channel expected patch log likelihood for color image denoising

作者：Zhou, Xiuling[1];Xu, Bingxin[2];Guo, Ping[3];He, Ning[2]

第一作者：Zhou, Xiuling

通讯作者：Zhou, XL[1]

机构：[1]Beijing City Univ, Dept Technol & Ind Dev, Beijing 100083, Peoples R China;[2]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China;[3]Beijing Normal Univ, Lab Image Proc & Pattern Recognit, Beijing 100875, Peoples R China

第一机构：Beijing City Univ, Dept Technol & Ind Dev, Beijing 100083, Peoples R China

通讯机构：[1]corresponding author), Beijing City Univ, Dept Technol & Ind Dev, Beijing 100083, Peoples R China.

年份：2019

卷号：367

起止页码：130-143

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20193407328714);Scopus(收录号：2-s2.0-85070675467);WOS:【SCI-EXPANDED(收录号:WOS:000489017500013)】；

基金：This work is fully supported by the grants from Beijing Natural Science Foundation (Project No. 4162027 and No. 4184088), the Joint Research Fund in Astronomy(U1531242) under cooperative agreement between the National Natural Science Foundation of China (NSFC) and Chinese Academy of Sciences (CAS), the National Natural Science Foundation of China (Grant Nos. 61572077, 61872042, 61370138) and the Key Projects of Beijing Municipal Education Commission (Project No. KZ201911417048).

语种：英文

外文关键词：Expected patch log-likelihood; Multi-channel; Matrix normal distribution; Color image denoising

摘要：Due to its flexibility and good restoration performance, the Expected Patch Log Likelihood (EPLL) method has attracted extensive attention and has been further developed. However, the basic EPLL method is mainly applied for gray image restoration. For color image denoising with different channel noise levels, concatenating the RGB values into a vector and applying the basic EPLL directly can produce false colors and artifacts. In this paper, a Multi-Channel Expected Patch Log Likelihood (MC-EPLL) method is proposed for color image denoising with different channel noise levels. Considering the within and between channel correlation, the noise model of the concatenated vector of RGB channels can be constructed as a Matrix Normal Distribution. Under the KL divergence framework, the MC-EPLL model can be derived by combining the noise model and Gaussian Mixture Model (GMM) based patch prior. Based on the half quadratic splitting (HQS) strategy, the MC-EPLL model is decomposed into two sub-minimization problems and the closed-form solution of each sub-problem can be obtained. Experiments show the feasibility and superiority of the proposed MC-EPLL over the compared denoising methods. (C) 2019 Elsevier B.V. All rights reserved.