计算机应用研究2017，Vol.34Issue(3)：949-952,4.DOI:10.3969/j.issn.1001-3695.2017.03.072
基于低秩和稀疏性先验知识的压缩感知图像重构
Compressed sensing image reconstruction based on low-rank and sparse prior
摘要
Abstract
The NLR algorithm which exploits low-rank prior and shows the state-of-the-art performance ignores image local structural information and cannot effectively reconstruct the edges.In order to improve the reconstruction precision with the same number of measurements,this paper introduced the sparisty regularization as the additional prior information of image,and proposed a total variation and low-rank property based CS image reconstruction model.It used the augmented Lagrange methodalternating direction method to solve the resulting non-convex optimization problem.Compared with the traditional sparisty regularized algorithms and NLR method,the proposed algorithm can achieve better image reconstruction results.关键词
压缩感知/稀疏表示/总变差/低秩属性Key words
compressive sensing/sparse representation/total variation/low-rank property分类
信息技术与安全科学引用本文复制引用
陈长伟,朱俊..基于低秩和稀疏性先验知识的压缩感知图像重构[J].计算机应用研究,2017,34(3):949-952,4.基金项目
金陵科技学院博士启动基金资助项目(Jit-b-201508) （Jit-b-201508）
