基于压缩感知的加速前向后向匹配追踪算法

doi:10.11999/JEIT151422

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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.

Key words： Compressed Sensing (CS) Greedy algorithms Forward-backward search Sparse signal reconstruction

Received: 14 December 2015 Published: 04 July 2016

PACS:

TN911.72

Fund:

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

Corresponding Authors: SUN Guiling E-mail: sungl@nankai.edu.cn

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	WANG Feng
	SUN Guiling
	ZHANG Jianping
	HE Jingfei

Cite this article:

WANG Feng,SUN Guiling,ZHANG Jianping等. Acceleration Forward-backward Pursuit Algorithm Based on Compressed Sensing[J]. JEIT, 2016, 38(10): 2538-2545.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT151422 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I10/2538

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