Compressed ghost imaging based on differential speckle patterns

doi:10.1088/1674-1056/ab671a

Abstract We propose a compressed ghost imaging scheme based on differential speckle patterns, named CGI-DSP. In the scheme, a series of bucket detector signals are acquired when a series of random speckle patterns are employed to illuminate an unknown object. Then the differential speckle patterns (differential bucket detector signals) are obtained by taking the difference between present random speckle patterns (present bucket detector signals) and previous random speckle patterns (previous bucket detector signals). Finally, the image of object can be obtained directly by performing the compressed sensing algorithm on the differential speckle patterns and differential bucket detector signals. The experimental and simulated results reveal that CGI-DSP can improve the imaging quality and reduce the number of measurements comparing with the traditional compressed ghost imaging schemes because our scheme can remove the environmental illuminations efficiently.

Keywords: ghost imaging compressed sensing differential speckle patterns differential bucket detector signals

Received: 21 October 2019
Revised: 17 November 2019
Accepted manuscript online:

PACS:	42.30.Va	(Image forming and processing)
	42.30.Wb	(Image reconstruction; tomography)

Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 11847062 and 61871234), the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20180755), and the Science Fund from NUPT (Grant No. NY218098).

Corresponding Authors: Shengmei Zhao
E-mail: zhaosm@njupt.edu.cn

Cite this article:

Le Wang(王乐), Shengmei Zhao(赵生妹) Compressed ghost imaging based on differential speckle patterns 2020 Chin. Phys. B 29 024204

[1]	Strekalov D V, Sergienko A V, Klyshko D N and Shih Y H 1995 Phys. Rev. Lett. 74 3600
[2]	Bennink R S, Bentley S J and Boyd R W 2002 Phys. Rev. Lett. 89 113601
[3]	Cao D Z, Xu B L, Zhang S H and Wang K G 2015 Chin. Phys. Lett. 32 114208
[4]	Li H X, Bai Y F, Shi X H, Nan S Q, Qu L J, Shen Q and Fu X Q 2017 Chin. Phys. B 26 104204
[5]	Wang L, Zou L and Zhao S 2018 Opt. Commun. 407 181
[6]	Yin M Q, Wang L and Zhao S M 2019 Chin. Phys. B 28 094201
[7]	Liu J, Wang J, Chen H, Zheng H, Liu Y, Zhou Y, Li F and Xu Z 2018 Opt. Commun. 410 824
[8]	Katz O, Bromberg Y and Silberberg Y 2009 Appl. Phys. Lett. 95 131110
[9]	Candés E J, Romberg J K and Tao T 2006 Commun. Pur. Appl. Math. 59 1207
[10]	Sun M J, Meng L T, Edgar M P, Padgett M J and Radwell N A 2017 Sci. Rep. 7 3464
[11]	Gong W, Zhao C, Yu H, Chen M, Xu W and Han S 2016 Sci. Rep. 6 26133
[12]	Zhao S and Zhuang P 2014 Chin. Phys. B 23 054203
[13]	Zhao S, Wang L, Liang W, Cheng W and Gong L 2015 Opt. Commun. 353 90
[14]	Wang L, Zhao S, Cheng W, Gong L and Chen H 2016 Opt. Commun. 366 314
[15]	Yu W K, Yao X R, Liu X F, Li L Z and Zhai G J 2015 Appl. Opt. 54 363
[16]	Wang L and Zhao S 2016 Photon. Res. 4 240
[17]	Welsh S S, Edgar M P, Bowman R, Sun B and Padgett M J 2015 J. Opt. 17 025705
[18]	Li P, Hastie T J and Church K W 2006 Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, August 20-23, 2006, Philadelphia, PA, USA, pp. 287-296
[19]	Achlioptas D 2003 J. Comput. Syst. Sci. 66 671
[20]	Li C B 2010 An efficient algorithm for total variation regularization with applications to the single pixel camera and compressive sensing (Master Dissertation) (Houston: Rice University)

[1]	A probability theory for filtered ghost imaging Zhong-Yuan Liu(刘忠源), Shao-Ying Meng(孟少英), and Xi-Hao Chen(陈希浩)^†. Chin. Phys. B, 2023, 32(4): 044204.
[2]	Ghost imaging based on the control of light source bandwidth Zhao-Qi Liu(刘兆骐), Yan-Feng Bai(白艳锋), Xuan-Peng-Fan Zou(邹璇彭凡), Li-Yu Zhou(周立宇), Qin Fu(付芹), and Xi-Quan Fu(傅喜泉). Chin. Phys. B, 2023, 32(3): 034210.
[3]	Imaging a periodic moving/state-changed object with Hadamard-based computational ghost imaging Hui Guo(郭辉), Le Wang(王乐), and Sheng-Mei Zhao(赵生妹). Chin. Phys. B, 2022, 31(8): 084201.
[4]	Orthogonal-triangular decomposition ghost imaging Jin-Fen Liu(刘进芬), Le Wang(王乐), and Sheng-Mei Zhao(赵生妹). Chin. Phys. B, 2022, 31(8): 084202.
[5]	Efficient implementation of x-ray ghost imaging based on a modified compressive sensing algorithm Haipeng Zhang(张海鹏), Ke Li(李可), Changzhe Zhao(赵昌哲), Jie Tang(汤杰), and Tiqiao Xiao(肖体乔). Chin. Phys. B, 2022, 31(6): 064202.
[6]	Iterative filtered ghost imaging Shao-Ying Meng(孟少英), Mei-Yi Chen(陈美伊), Jie Ji(季杰), Wei-Wei Shi(史伟伟), Qiang Fu(付强), Qian-Qian Bao(鲍倩倩), Xi-Hao Chen(陈希浩), and Ling-An Wu(吴令安). Chin. Phys. B, 2022, 31(2): 028702.
[7]	Full color ghost imaging by using both time and code division multiplexing technologies Le Wang(王乐), Hui Guo(郭辉), and Shengmei Zhao(赵生妹). Chin. Phys. B, 2022, 31(11): 114202.
[8]	High speed ghost imaging based on a heuristic algorithm and deep learning Yi-Yi Huang(黄祎祎), Chen Ou-Yang(欧阳琛), Ke Fang(方可), Yu-Feng Dong(董玉峰), Jie Zhang(张杰), Li-Ming Chen(陈黎明), and Ling-An Wu(吴令安). Chin. Phys. B, 2021, 30(6): 064202.
[9]	Handwritten digit recognition based on ghost imaging with deep learning Xing He(何行), Sheng-Mei Zhao(赵生妹), and Le Wang(王乐). Chin. Phys. B, 2021, 30(5): 054201.
[10]	Identification of denatured and normal biological tissues based on compressed sensing and refined composite multi-scale fuzzy entropy during high intensity focused ultrasound treatment Shang-Qu Yan(颜上取), Han Zhang(张含), Bei Liu(刘备), Hao Tang(汤昊), and Sheng-You Qian(钱盛友). Chin. Phys. B, 2021, 30(2): 028704.
[11]	Ghost imaging-based optical cryptosystem for multiple images using integral property of the Fourier transform Yi Kang(康祎), Leihong Zhang(张雷洪), Hualong Ye(叶华龙), Dawei Zhang(张大伟), and Songlin Zhuang(庄松林). Chin. Phys. B, 2021, 30(12): 124207.
[12]	Computational ghost imaging with deep compressed sensing Hao Zhang(张浩), Yunjie Xia(夏云杰), and Deyang Duan(段德洋). Chin. Phys. B, 2021, 30(12): 124209.
[13]	Compressive imaging based on multi-scale modulation and reconstruction in spatial frequency domain Fan Liu(刘璠), Xue-Feng Liu(刘雪峰), Ruo-Ming Lan(蓝若明), Xu-Ri Yao(姚旭日), Shen-Cheng Dou(窦申成), Xiao-Qing Wang(王小庆), and Guang-Jie Zhai(翟光杰). Chin. Phys. B, 2021, 30(1): 014208.
[14]	An image compressed sensing algorithm based on adaptive nonlinear network Yuan Guo(郭媛), Wei Chen(陈炜), Shi-Wei Jing(敬世伟). Chin. Phys. B, 2020, 29(5): 054203.
[15]	Super-resolution filtered ghost imaging with compressed sensing Shao-Ying Meng(孟少英), Wei-Wei Shi(史伟伟), Jie Ji(季杰), Jun-Jie Tao(陶俊杰), Qian Fu(付强), Xi-Hao Chen(陈希浩), and Ling-An Wu(吴令安). Chin. Phys. B, 2020, 29(12): 128704.

No Suggested Reading articles found!

Viewed

Full text

Abstract

Cited

Metrics
Related Articles

Compressed ghost imaging based on differential speckle patterns

Cite this article:

Online attention