Chinese Optics Letters, Volume. 19, Issue 10, 101101(2021)

Computational ghost imaging with compressed sensing based on a convolutional neural network

Hao Zhang1 and Deyang Duan1,2、*
Author Affiliations
  • 1School of Physics and Physical Engineering, Qufu Normal University, Qufu 273165, China
  • 2Shandong Provincial Key Laboratory of Laser Polarization and Information Technology, Research Institute of Laser, Qufu Normal University, Qufu 273165, China
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    References(26)

    [1] T. B. Pittman, Y. H. Shih, D. V. Strekalov, A. V. Sergienko. Optical imaging by means of two-photon quantum entanglement. Phys. Rev. A, 52, R3429(1995).

    [2] J. Cheng, S.-S. Han. Incoherent coincidence imaging and its applicability in X-ray diffraction. Phys. Rev. Lett., 92, 093903(2004).

    [3] X. H. Chen, Q. Liu, K. H. Luo, L. A. Wu. Lensless ghost imaging with true thermal light. Opt. Lett., 34, 695(2009).

    [4] B. I. Erkmen. Computational ghost imaging for remote sensing. J. Opt. Soc. A, 29, 782(2012).

    [5] D. Y. Duan, Z. X. Man, Y. J. Xia. Nondegenerate wavelength computational ghost imaging with thermal light. Opt. Express, 27, 25187(2019).

    [6] J. H. Gu, S. Sun, Y. K. Xu, H. Z. Lin, W. T. Liu. Feedback ghost imaging by gradually distinguishing and concentrating onto the edge area. Chin. Opt. Lett., 19, 041102(2021).

    [7] G. Wang, H. B. Zheng, Z. G. Tang, Y. C. He, Y. Zhou, H. Chen, J. B. Liu, Y. Yuan, F. L. Li, Z. Xu. Naked-eye ghost imaging via photoelectric feedback. Chin. Opt. Lett., 18, 091101(2020).

    [8] D. Pelliccia, A. Rack, M. Scheel, V. Cantelli, D. M. Paganin. Experimental X-ray ghost imaging. Phys. Rev. Lett., 117, 113902(2016).

    [9] H. Yu, R. Lu, S. Han, H. Xie, G. Du, T. Xiao, D. Zhu. Fourier-transform ghost imaging with hard X rays. Phys. Rev. Lett., 117, 113901(2016).

    [10] A. Zhang, Y. He, L. Wu, L. Chen, B. Wang. Tabletop X-ray ghost imaging with ultra-low radiation. Optica, 5, 374(2018).

    [11] W. Li, Z. Tong, K. Xiao, Z. Liu, Q. Gao, J. Sun, S. Liu, S. Han, Z. Wang. Single-frame wide-field nanoscopy based on ghost imaging via sparsity constraints. Optica, 6, 1515(2019).

    [12] W. Gong, S. Han. High-resolution far-field ghost imaging via sparsity constraint. Sci. Rep., 5, 9280(2015).

    [13] J. H. Shapiro. Computational ghost imaging. Phys. Rev. A, 78, 061802(R)(2008).

    [14] Y. Bromberg, O. Katz, Y. Silberberg. Ghost imaging with a single detector. Phys. Rev. A, 79, 053840(2009).

    [15] O. Katza, Y. Bromberg, Y. Silberberg. Compressive ghost imaging. Appl. Phys. Lett., 95, 131110(2009).

    [16] V. Katkovnik, J. Astola. Compressive sensing computational ghost imaging. J. Opt. Soc. Am. A, 29, 1556(2012).

    [17] P. W. Wang, C. L. Wang, C. P. Yu, S. Yue, W. L. Gong, S. S. Han. Color ghost imaging via sparsity constraint and non-local self-similarity. Chin. Opt. Lett., 19, 021102(2021).

    [18] Z. Chen, J. Shi, G. Zeng. Object authentication based on compressive ghost imaging. Appl. Opt., 55, 8644(2016).

    [19] M. Lyu, W. Wang, H. Wang, W. Wang, G. Li, N. Chen, G. Situ. Deep-learning-based ghost imaging. Sci. Rep., 7, 17865(2017).

    [20] Y. He, G. Wang, G. Dong, S. Zhu, H. Chen, A. Zhang, Z. Xu. Ghost imaging based on deep learning. Sci. Rep., 8, 6469(2018).

    [21] T. Shimobaba, Y. Endo, T. Nishitsuji, T. Takahashi, Y. Nagahama, T. Hasegawa, M. Sano, R. Hirayama, T. Kakue, A. Shiraki, T. Ito. Computational ghost imaging using deep learning. Opt. Commun., 413, 147(2018).

    [22] G. Barbastathis, A. Ozcan, G. Situ. On the use of deep learning for computational imaging. Optica, 6, 921(2019).

    [23] X. L. Yin, Y. J. Xia, D. Y. Duan. Theoretical and experimental study of the color of ghost imaging. Opt. Express, 26, 18944(2018).

    [24] W. J. Jiang, X. Y. Li, X. L. Peng, B. Q. Sun. Imaging high-speed moving targets with a single-pixel detector. Opt. Express, 28, 7889(2020).

    [25] D. F. Shi, C. Y. Fan, P. F. Zhang, H. Shen, J. H. Zhang, C. H. Qiao, Y. J. Wang. Two-wavelength ghost imaging through atmospheric turbulence. Opt. Express, 21, 2050(2013).

    [26] Y. H. Liu, S. Y. Liu, F. X. Fu. Optimization of compressed sensing reconstruction algorithms based on convolutional neural network. Comput. Sci., 47, 143(2020).

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    Hao Zhang, Deyang Duan, "Computational ghost imaging with compressed sensing based on a convolutional neural network," Chin. Opt. Lett. 19, 101101 (2021)

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    Paper Information

    Category: Imaging Systems and Image Processing

    Received: Jan. 4, 2021

    Accepted: Mar. 26, 2021

    Posted: Mar. 29, 2021

    Published Online: Aug. 16, 2021

    The Author Email: Deyang Duan (duandy2015@qfnu.edu.cn)

    DOI:10.3788/COL202119.101101

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