ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS |
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High speed ghost imaging based on a heuristic algorithm and deep learning |
Yi-Yi Huang(黄祎祎)1,2, Chen Ou-Yang(欧阳琛)1,2, Ke Fang(方可)1,2, Yu-Feng Dong(董玉峰)1, Jie Zhang(张杰)1,3, Li-Ming Chen(陈黎明)3,4,†, and Ling-An Wu(吴令安)1,2,‡ |
1 Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China; 2 University of Chinese Academy of Sciences, Beijing 100049, China; 3 IFSA Collaborative Innovation Center and School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China; 4 College of Engineering Physics, Shenzhen Technology University, Shenzhen 518118, China |
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Abstract We report an overlapping sampling scheme to accelerate computational ghost imaging for imaging moving targets, based on reordering a set of Hadamard modulation matrices by means of a heuristic algorithm. The new condensed overlapped matrices are then designed to shorten and optimize encoding of the overlapped patterns, which are shown to be much superior to the random matrices. In addition, we apply deep learning to image the target, and use the signal acquired by the bucket detector and corresponding real image to train the neural network. Detailed comparisons show that our new method can improve the imaging speed by as much as an order of magnitude, and improve the image quality as well.
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Received: 17 December 2020
Revised: 10 February 2021
Accepted manuscript online: 01 March 2021
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PACS:
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42.30.-d
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(Imaging and optical processing)
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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 Key Research and Development Program of China (Grant Nos. 2017YFA0403301, 2017YFB0503301, and 2018YFB0504302), the National Natural Science Foundation of China (Grant Nos. 11991073, 61975229, and Y8JC011L51), the Key Program of CAS (Grant No. XDB17030500), the Civil Space Project (Grant No. D040301), and the Science Challenge Project (Grant No. TZ2018005). |
Corresponding Authors:
Li-Ming Chen, Ling-An Wu
E-mail: lmchen@sjtu.edu.cn;wula@iphy.ac.cn
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Cite this article:
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) High speed ghost imaging based on a heuristic algorithm and deep learning 2021 Chin. Phys. B 30 064202
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[1] Pittman T B, Shih Y H, Strekalov D V and Sergienko A V 1995 Phys. Rev. A 52 R3429 [2] Bennink R S, Bentley S J and Boyd R W 2002 Phys. Rev. Lett. 89 113601 [3] Cheng J and Han S S 2004 Phys. Rev. Lett. 92 093903 [4] Schori A and Shwartz S 2017 Opt. Express 25 14822 [5] Zhang A X, He Y H, Wu L A, Chen L M and Wang B B 2018 Optica 5 374 [6] Li S, Cropp F, Kabra K, Lane T J, Wetzstein G, Musumeci P and Ratner D 2018 Phys. Rev. Lett. 121 114801 [7] Khakimov R I, Henson B M, Shin D K, Hodgman S S, dall R G, Baldwin K G H and Truscott A G 2016 Nature 540 100 [8] He Y H, Huang Y Y, Zeng Z R, Li Y F, Tan J H, Chen L M, Wu L A, Li M F, Quan B G, Wang S L and Liang T J 2021 Science Bulletin 66 133 [9] Bromberg Y, Katz O and Silberberg Y 2009 Phys. Rev. A 79 053840 [10] Shapiro J H 2008 Phys. Rev. A 78 061802 [11] Morris P A, Aspden R S, Bell J E C et al. 2015 Nat. Commun. 6 5913 [12] Janassek P, Blumenstein S and Elsäßer W 2018 Phys. Rev. Applied 9 2 [13] Tanha M, Ahmadi-Kandjani S and Kheradmand R 2013 Physica Scripta T157 014059 [14] Tanha M, Kheradmand R and Ahmadi-Kandjani S 2012 Appl. Phys. Lett. 101 101108 [15] Erkmen B I 2012 J. Opt. Soc. Am. A 29 782 [16] Li E R, Bo Z W, Chen M L, Gong W L and Han S S 2014 Appl. Phys. Lett. 104 251120 [17] Erkmen B I and Shapiro J H 2009 Phys. Rev. A 79 023833 [18] Edgar M P, Gibson G M and Padgett M J 2019 Nat. Photon. 13 13 [19] Wang Y, Liu Y, Suo J, Situ G, Qiao C and Dai Q 2017 Sci. Rep. 7 45325 [20] Katz O, Bromberg Y and Silberberg Y 2009 Appl. Phys. Lett. 95 131110 [21] Pratt W K, Kane J and Andrews H C 1969 Proc. IEEE 57 58 [22] Sun M J, Meng L T, Edgar M P, Padgett M J and Radwell N 2017 Sci. Rep. 7 3464 [23] Higham C F, Murray-Smith R, Padgett M J and Edgar M P 2018 Sci. Rep. 8 2369 [24] Yu W K 2019 Sensors 19 4122 [25] Yu W K and Liu Y M 2019 Sensors 19 5135 [26] Zhao C Q, Gong W L, Chen M L, Li E R, Wang H, Xu W D and Han S S 2012 Appl. Phys. Lett. 101 141123 [27] Wang W, Hu X M, Liu J D, Zhang S Z, Suo J L and Situ G H 2015 Opt. Express 23 28416 [28] Wang L and Zhao S M 2020 Chin. Phys. B 29 024204 [29] Wang W, Wang Y P, Li J, Yang X and Wu Y 2014 Opt. Lett. 39 5150 [30] Kamilov U S, Papadopoulos I N, Shoreh M H, Goy A, Vonesch C, Unser M and Psaltis D 2015 Optica 2 517 [31] Li Y X, Yu W K, Leng J and Wang S F 2019 Opt. Express 27 35166 [32] Lyu M, Wang W, Wang H, Wang H C, Li G W, Ni Chen and Situ G H 2017 Sci. Rep. 7 17865 [33] He Y C, Wang G, Dong G X, Zhu S T, Chen H, Zhang A X and Xu Z 2018 Sci. Rep. 8 6469 [34] Wang F, Wang H, Wang H C, Li G W and Situ G H 2019 Opt. Express 27 25560 [35] Wu H, Wang R Z, Zhao G P, Xiao H P, Liang J, Wang D D, Tian X B, Cheng L L and Zhang X M 2020 Optics and Lasers in Engineering 134 106183 [36] Zhang Z B, Li X, Zheng S J, Yao M H, Zheng G A and Zhong J G 2020 Opt. Express 28 13269 [37] Gao Z Q, Cheng X M, Chen K, Wang A Q, Hu Y, Zhang S H and Hao Q 2020 IEEE Photonics Journal 12 1 [38] Kingston A M, Fullagar W K, Myers G R, Adams D, Pelliccia D and Paganin D M 2020 arXiv:2009.02139 [39] Li H, Xiong J and Zeng G H 2011 Opt. Engineering 50 127005 [40] Tropp J A and Gilbert A C 2007 IEEE Transactions on Information Theory 53 4655 [41] Chan K W C, O'Sullivan M N and Boyd R W 2010 Opt. Express 18 5562 [42] Mccann M T, Jin K H and Unser M 2017 IEEE Signal Processing Magazine 34 6 [43] Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H and Bengio Y 2014 Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) pp. 1724–1734 [44] Donahue J, Hendricks L A, Guadarrama S, Rohrbach M, Venugopalan S, Saenko K and Darrell T 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 2625–2634 [45] Sutskever I, Vinyals O and Le Q V 2014 arXiv:1409.3215 [cs.CL] [46] He K M, Zhang X Y, Ren S Q and Sun J 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 770–778 [47] Simonyan K and Zisserman A2014 arXiv:1409.1556v6 [CoRR] [48] Bochkovskiy A, Wang C Y and Liao H Y M 2020 arXiv:2004.10934v1[CoRR] |
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