Compressed image sensing with components regularization based on Bregman iteration  

作　　者：LI Xing-xiu WEI Zhi-hui XIAO Liang 

机构地区：[1]School of Science, Nanjing University of Science and Technology, Nanjing 210094, China [2]School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China

出　　处：《The Journal of China Universities of Posts and Telecommunications》2011年第2期114-119,共6页中国邮电高校学报（英文版）

基　　金：supported by the National Nature Science Foundation of China (60802039, 61071146);the Specialized Research Fund for the Doctoral Program of Higher Education of China (200802880018);NUST Research Funding (2010ZDJH07);the Natural Science Foundation of Jiangsu (SBK201022367);Qing Lan Project of Jiangsu Province

摘　　要：Compressed sensing（CS） is a new theory of signal processing for simultaneous signal sampling and compression.The optimization methods with components regularization have been proposed to perform CS reconstruction of the natural images which always contain various morphological components.In this paper,in order to solve the components regularized optimization problem more accurately,an iterative algorithm is proposed based on the Bregman iteration.The proposed algorithm is an inner-outer iterative procedure,with the two-variable Bregman iteration as its outer iteration and the alternating minimization as its inner iteration.Experimental results show the superiority of the proposed algorithm to other recently developed algorithms in terms of the visual quality improvement and the detail feature preserving capability.Compressed sensing（CS） is a new theory of signal processing for simultaneous signal sampling and compression.The optimization methods with components regularization have been proposed to perform CS reconstruction of the natural images which always contain various morphological components.In this paper,in order to solve the components regularized optimization problem more accurately,an iterative algorithm is proposed based on the Bregman iteration.The proposed algorithm is an inner-outer iterative procedure,with the two-variable Bregman iteration as its outer iteration and the alternating minimization as its inner iteration.Experimental results show the superiority of the proposed algorithm to other recently developed algorithms in terms of the visual quality improvement and the detail feature preserving capability.

关 键 词：CS image reconstruction Bregman iteration components regularization 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置] O177.91[自动化与计算机技术—控制科学与工程]