A Modified Image Reconstruction Algorithm Based on Compressed Sensing

被引：0

作者：

Wang, Aili ^{[1
]}

Gao, Xue ^{[1
]}

Gao, Yue ^{[1
]}

机构：

[1] Harbin Univ Sci & Technol, Higher Educ Key Lab Measuring & Control Technol &, Harbin, Peoples R China

来源：

2014 FOURTH INTERNATIONAL CONFERENCE ON INSTRUMENTATION AND MEASUREMENT, COMPUTER, COMMUNICATION AND CONTROL (IMCCC) | 2014年

关键词：

matching pursuit; compressed sensing; sparse representation; reconstruction algorithm;

D O I：

10.1109/IMCCC.2014.133

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Compressed sensing theory is a new kind of making full use of signal sparsity or compressible sampling theory. The theory suggests that collecting a small amount of signal values can realize accurate reconstruction of sparse or compressed signal. Through the research and summary of the existing reconstruction algorithm, the paper proposes a new adaptive matching pursuit algorithm based on regularization Regularized Adaptive Matching Pursuit(RAMP) for compressed sensing signal reconstruction, called blocking sparsity adaptive regularized matching pursuit (BSARMP) algorithms. In order to reduce the scale of a single observation matrix processing and the single processing speed, a novel method based on image blocking is presented in this paper, thereby improving the overall running time.

引用

页码：624 / 627

页数：4

共 11 条

[1]

Candes E. J., 2006, P INT C MATH MADR SP, V3, P1433, DOI DOI 10.4171/022-3/69

[2] Robust uncertainty principles:: Exact signal reconstruction from highly incomplete frequency information [J].