计算机工程与应用Issue(14)：203-206,4.DOI:10.3778/j.issn.1002-8331.1202-0550
基于结构化稀疏模型的压缩感知重构改进算法
New recovery algorithm for compressed sensing based on structured sparse model.
摘要
Abstract
Recently, normal recovery algorithms for CS only use signal and image sparse priors under wavelet, make no use of the tree structure priors. In order to reconstruct the original signal quickly and accurately, this paper brings the tree structure sparse model into SP algorithm , CoSaMP-algorithm and gets the improved recovery algorithm for compressed sensing. Combin-ing with structured sparse model and dual-tree complex wavelet transform, a new recovery algorithm for CS is proposed. The simulated results show that the algorithm can achieve higher reconstructed image performance.关键词
压缩感知/结构化稀疏模型/双树复小波变换Key words
Compressed Sensing(CS)/structured sparse model/dual-tree complex wavelet transform分类
信息技术与安全科学引用本文复制引用
杨爱萍,栗改,侯正信,庞茜..基于结构化稀疏模型的压缩感知重构改进算法[J].计算机工程与应用,2013,(14):203-206,4.基金项目
国家自然科学基金（No.61002027）。 （）
