Compressed sensing sparse reconstruction for coherent field imaging

doi:10.1088/1674-1056/25/4/040701

Abstract Return signal processing and reconstruction plays a pivotal role in coherent field imaging, having a significant influence on the quality of the reconstructed image. To reduce the required samples and accelerate the sampling process, we propose a genuine sparse reconstruction scheme based on compressed sensing theory. By analyzing the sparsity of the received signal in the Fourier spectrum domain, we accomplish an effective random projection and then reconstruct the return signal from as little as 10% of traditional samples, finally acquiring the target image precisely. The results of the numerical simulations and practical experiments verify the correctness of the proposed method, providing an efficient processing approach for imaging fast-moving targets in the future.

Keywords: coherent field imaging computational imaging sparse reconstruction compressed sensing

Received: 06 November 2015
Revised: 22 December 2015
Accepted manuscript online:

PACS:	07.05.Hd	(Data acquisition: hardware and software)
	43.60.Hj	(Time-frequency signal processing, wavelets)
	07.05.Tp	(Computer modeling and simulation)

Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61505248) and the Fund from Chinese Academy of Sciences, the Light of “Western” Talent Cultivation Plan “Dr. Western Fund Project” (Grant No. Y429621213).

Corresponding Authors: Bei Cao
E-mail: candy@opt.ac.cn

Cite this article:

Bei Cao(曹蓓), Xiu-Juan Luo(罗秀娟), Yu Zhang(张羽), Hui Liu(刘辉), Ming-Lai Chen(陈明徕) Compressed sensing sparse reconstruction for coherent field imaging 2016 Chin. Phys. B 25 040701

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Compressed sensing sparse reconstruction for coherent field imaging

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