| 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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