Acceleration Forward-backward Pursuit Algorithm Based on Compressed Sensing

WANG Feng; SUN Guiling; ZHANG Jianping; HE Jingfei

doi:10.11999/JEIT151422

Volume 38 Issue 10

Oct. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(10): 2538-2545

WANG Feng, SUN Guiling, ZHANG Jianping, HE Jingfei. Acceleration Forward-backward Pursuit Algorithm Based on Compressed Sensing[J]. Journal of Electronics & Information Technology, 2016, 38(10): 2538-2545. doi: 10.11999/JEIT151422

Citation:

WANG Feng, SUN Guiling, ZHANG Jianping, HE Jingfei. Acceleration Forward-backward Pursuit Algorithm Based on Compressed Sensing[J]. Journal of Electronics & Information Technology, 2016, 38(10): 2538-2545. doi: 10.11999/JEIT151422

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Acceleration Forward-backward Pursuit Algorithm Based on Compressed Sensing

doi: 10.11999/JEIT151422

1.
(College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China)
2.
(School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai 200030, China)

Funds:

The National Natural Science Foundation of China (61171140), The Doctoral Program of Higher Education (20130031110032)

Received Date: 2015-12-14
Rev Recd Date: 2016-05-05
Publish Date: 2016-10-19

Abstract

Abstract

The Forward-Backward Pursuit (FBP) algorithm, a novel two stage greedy approach, receives wide attention due to the high reconstruction accuracy and the feature without prior information of the sparsity. However, FBP has to run more time to get a higher precision. To alleviate this drawback, this paper proposes the Acceleration Forward-Backward Pursuit (AFBP) algorithm based on Compressed Sensing (CS). In order to reduce the number of iterations, the algorithm exploits the information available in the support estimate to add the deleted atoms again. The run time of AFBP is sharply shorter than that of FBP, while the precision of AFBP is not lower than FBP. The efficacy of the proposed scheme is demonstrated by simulations using random sparse signals with different nonzero coefficient distributions and a sparse image.
- Compressed Sensing (CS),
- Greedy algorithms,
- Forward-backward search,
- Sparse signal reconstruction

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References(19)

References

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