Compressed sensing sparse reconstruction for coherent field imaging

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

中国物理B ›› 2016, Vol. 25 ›› Issue (4): 40701-040701.doi: 10.1088/1674-1056/25/4/040701

Compressed sensing sparse reconstruction for coherent field imaging

Bei Cao(曹蓓), Xiu-Juan Luo(罗秀娟), Yu Zhang(张羽), Hui Liu(刘辉), Ming-Lai Chen(陈明徕)

Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China

收稿日期:2015-11-06 修回日期:2015-12-22 出版日期:2016-04-05 发布日期:2016-04-05
通讯作者: Bei Cao E-mail:candy@opt.ac.cn
基金资助:
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).

Compressed sensing sparse reconstruction for coherent field imaging

Bei Cao(曹蓓), Xiu-Juan Luo(罗秀娟), Yu Zhang(张羽), Hui Liu(刘辉), Ming-Lai Chen(陈明徕)

Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China

Received:2015-11-06 Revised:2015-12-22 Online:2016-04-05 Published:2016-04-05
Contact: Bei Cao E-mail:candy@opt.ac.cn
Supported by:
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).

摘要/Abstract

摘要： 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.

关键词: coherent field imaging, computational imaging, sparse reconstruction, compressed sensing

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.

Key words: coherent field imaging, computational imaging, sparse reconstruction, compressed sensing

中图分类号: (Data acquisition: hardware and software)

07.05.Hd

43.60.Hj (Time-frequency signal processing, wavelets) 07.05.Tp (Computer modeling and simulation)

曹蓓, 罗秀娟, 张羽, 刘辉, 陈明徕. Compressed sensing sparse reconstruction for coherent field imaging[J]. 中国物理B, 2016, 25(4): 40701-040701.

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[J]. Chin. Phys. B, 2016, 25(4): 40701-040701.

参考文献 16

[1]	Holmes R B, Ma S, Bhowmik A and Greninger C 1996 J. Opt. Soc. Am. A 13 351
[2]	Holmes R B and Brinkley T 1999 Proc. SPIE 3815 11
[3]	Lu L, Ji X L, Deng J P and Li X Q 2014 Chin. Phys. B 23 064209
[4]	Qian X M, Zhu W Y and Rao R Z 2015 Chin. Phys. B 24 044201
[5]	Stapp J, Spivey B, Chen L, Leon L, Hughes K, Sandler D and Cuellar E L 2006 Proc. SPIE 6307 630701
[6]	Cuellar E L, Cooper J, Mathis J and Fairchild P 2008 Proc. SPIE 7094 70940G
[7]	Yu S H, Wang J L, Dong L, Liu X Y and Wang G C 2013 Acta Opt. Sin. 33 0811001 (in Chinese)
[8]	Dong L, Liu X Y, Liu J and Yu S H 2013 Acta Photon. Sin. 42 447 (in Chinese)
[9]	Donoho D L 2006 IEEE Trans. Inform. Theory 52 1289
[10]	Duarte M F, Davenport M A, Takhar D, Laska J N, Sun T, Kelly K F and Baraniuk R G 2008 IEEE Signal Processing Magazine 25 83
[11]	Baraniuk R G and Steeghs P 2007 IEEE Radar Conference, April, 2007, Waltham, MA, USA, pp. 128-133
[12]	Brady D, Choi K, Marks D, Horisaki R and Lim S 2009 Opt. Express 17 13040
[13]	Cand'es E J and Romberg J 2007 Inverse Probl. 23 969
[14]	Tropp J A and Gilbert A C 2007 IEEE Trans. Inform. Theory 53 4655
[15]	Cao B, Luo X J, Chen M L and Zhang Y 2015 Acta Phys. Sin. 64 124205 (in Chinese)
[16]	Cuellar E L, Stapp J and Cooper J 2005 Proc. SPIE 5896 58960D

Compressed sensing sparse reconstruction for coherent field imaging

Compressed sensing sparse reconstruction for coherent field imaging

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