ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS |
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Compressed ghost imaging based on differential speckle patterns |
Le Wang(王乐)1, Shengmei Zhao(赵生妹)1,2 |
1 Institute of Signal Processing and Transmission, Nanjing University of Posts and Telecommunications(NUPT), Nanjing 210003, China; 2 Key Laboratory of Broadband Wireless Communication and Sensor Network Technology(NUPT), Ministry of Education, Nanjing 210003, China |
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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.
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Received: 21 October 2019
Revised: 17 November 2019
Accepted manuscript online:
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PACS:
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42.30.Va
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(Image forming and processing)
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42.30.Wb
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(Image reconstruction; tomography)
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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
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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
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[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)
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