A compressed sensing image reconstruction algorithm based on block sparse bayesian learning and multiple measurement vector

被引：1

作者：

Yu, Jianqiao ^{[1
]}

Yue, Yongdou ^{[1
]}

机构：

[1] School of Computer and Information Sciences, Southwest University, No. 2, Tiansheng Road, Beibei District, Chongqing, China

来源：

ICIC Express Letters | 2015年 / 9卷 / 07期

关键词：

Compressed sensing - Image enhancement - Learning algorithms - Vectors;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

In this paper, we focus on the image reconstruction algorithms of com- pressed sensing theory based on sparse Bayesian learning and multiple measurement vector (SBL MMV). Considering that there is no information related to the internal structure of the sparse matrix solution in SBL MMV algorithm, so according to the latest proposed block sparse Bayesian learning framework, using correlation within the block after blocking solution matrix, we propose a compressed sensing image reconstruc- tion algorithm based on block sparse Bayesian learning and multiple measurement vector (BSBL MMV). Experiments show that the algorithm can improve the quality of the re- constructed image. © 2015, ISSN 1881-803X.

引用

页码：2009 / 2014