Laser & Optoelectronics Progress, Volume. 62, Issue 17, 1739009(2025)

Computational Ghost Imaging: From Classical Computation to Deep Learning Driven (Invited)

Yifan Chen1,2, Zhe Sun1,2、*, and Xuelong Li1,2、**
Author Affiliations
  • 1School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi'an 710072, Shaanxi , China
  • 2Institute of Artificial Intelligence, China Telecom, Shanghai 200232, China
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    Figures & Tables(12)
    Experimental setup of ghost imaging
    Experimental setup and result of entangled two-photon ghost imaging[12]. (a) Experimental setup; (b) experimental result
    Experimental setup and result of ghost imaging based on classical light source[30]. (a) Experimental setup; (b) experimental result
    Experimental setup of ghost imaging based on pseudo-thermal light source[14]
    Experimental setup of computational ghost imaging[50]
    Framework of computational ghost imaging using deep neural networks[93]
    Framework of computational ghost imaging using self-supervised neural networks[112]
    Framework of self-supervised feature extraction-based ghost imaging[119]
    Framework of part-based ghost imaging[120]
    Framework of multi-input mutual supervision ghost imaging[122]
    Framework of multi-polarization fusion network for ghost imaging[123]
    Framework of large model enhanced computational ghost imaging[124]. (a) Framework of algorithm; (b) network structure
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    Yifan Chen, Zhe Sun, Xuelong Li. Computational Ghost Imaging: From Classical Computation to Deep Learning Driven (Invited)[J]. Laser & Optoelectronics Progress, 2025, 62(17): 1739009

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    Paper Information

    Category: AI for Optics

    Received: Apr. 15, 2025

    Accepted: May. 26, 2025

    Published Online: Sep. 12, 2025

    The Author Email: Zhe Sun (sunzhe@nwpu.edu.cn), Xuelong Li (li@nwpu.edu.cn)

    DOI:10.3788/LOP251007

    CSTR:32186.14.LOP251007

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