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Proceedings Paper

A combination approach for compressed sensing signal reconstruction
Author(s): Yujie Zhang; Rui Qi; Yanni Zeng
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Paper Abstract

This paper presents a combination approach which fusing the estimates of forward backward pursuit (FBP) and backtracking-based adaptive orthogonal matching pursuit (BAOMP) to approximate sparse solutions for compressed sensing without the sparsity level as a prior. This algorithm referred to as combination approach for compressed sensing (CACS). It can improve the sparse signal recovery performance in a minimum number of measurements. Numerical experiments for both synthetic and real signals are conducted to demonstrate the validity and high performance of the proposed algorithm, as compared to the individual compressed sensing algorithms.

Paper Details

Date Published: 11 July 2016
PDF: 5 pages
Proc. SPIE 10011, First International Workshop on Pattern Recognition, 1001119 (11 July 2016); doi: 10.1117/12.2242863
Show Author Affiliations
Yujie Zhang, China Univ. of Geosciences (China)
Univ. of Windsor (Canada)
Rui Qi, China Univ. of Geosciences (China)
Naval Univ. of Engineering (China)
Yanni Zeng, China Univ. of Geosciences (China)
Hubei Univ. of Economics (China)

Published in SPIE Proceedings Vol. 10011:
First International Workshop on Pattern Recognition
Xudong Jiang; Guojian Chen; Genci Capi; Chiharu Ishll, Editor(s)

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