The Convex Relaxation Method on Deconvolution Model with Multiplicative Noise

作     者：Yumei Huang Michael Ng Tieyong Zeng 

作者机构：School of Mathematics and StatisticsLanzhou UniversityLanzhou 730000China Department of MathematicsHong Kong Baptist UniversityKowloon TongHong Kong 

出 版 物：《Communications in Computational Physics》 (计算物理通讯（英文）)

年 卷 期：2013年第13卷第4期

页      面：1066-1092页

核心收录：

学科分类：07[理学] 0704[理学-天文学] 0701[理学-数学] 0702[理学-物理学] 070101[理学-基础数学] 

基　　金：supported in part by:Hong Kong RGC 203109,211710,RGC 211911 the FRGs of Hong Kong Baptist University NSFC Grant No.11101195 and No.11171371 Specialized Research Fund for the Doctoral Program of Higher Education of China No.20090211120011 China Postdoctoral Science Foundation funded project No.2011M501488 

主　　题：Alternating minimization convergence deblurring multiplicative noise non-convex model 

摘      要：In this paper,we consider variational approaches to handle the multiplicative noise removal and deblurring *** on rather reasonable physical blurring-noisy assumptions,we derive a new variational model for this *** the study of the basic properties,we propose to approximate it by a convex relaxation model which is a balance between the previous non-convex model and a convex *** relaxed model is solved by an alternating minimization *** examples are presented to illustrate the effectiveness and efficiency of the proposed method.