数据采集与处理2016，Vol.31Issue(6)：1148-1155,8.
基于局部和非局部正则化的图像压缩感知
Image Compressed Sensing Based on Local and Nonlocal Regularizations
摘要
Abstract
Nonlocal low‐rank regularization based approach (NLR) shows the state‐of‐the‐art performance in compressive sensing (CS) recovery which exploits both structured sparsity of similar patches .Howev‐er ,it cannot efficiently preserve the edges because it only exploits the nonlocal regularization and ignores the relationship between pixels .Meanwhile ,Logdet function that is used in NLR cannot well approxi‐mate the rank ,because it is a fixed function and the optimization results obtained by this function essen‐tially deviate from the real solution .A local and nonlocal regularization based CS approach is proposed to‐ward exploiting the local sparse‐gradient property of image and low‐rank property of similar patches . Schatten‐p norm is used as a better non‐convex surrogate for the rank function .In addition ,the alterna‐ting direction method of multipliers method (ADMM ) is utilized to solve the resulting nonconvex optimi‐zation problem .Experimental results demonstrate that the proposed method outperforms existing state‐of‐the‐art CS algorithms for image recovery .关键词
压缩感知/总变差/低秩/交替方向乘子算法Key words
compressive sensing (CS)/total variation/low-rank/alternating direction method of multi-pliers分类
信息技术与安全科学引用本文复制引用
朱俊,陈长伟,苏守宝,常子楠..基于局部和非局部正则化的图像压缩感知[J].数据采集与处理,2016,31(6):1148-1155,8.基金项目
江苏省高等学校自然科学研究面上（16KJB520014，14KJB520012）资助项目；江苏省社会安全图像与视频理解重点实验室（30916014107）资助项目；国家自然科学基金（61375121）资助项目；金陵科技学院博士启动资金资助项目。 （）
