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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    References(124)

    [2] Lin H Z, Liu W T, Sun S et al. Progress of ghost imaging algorithms[J]. Chinese Journal of Quantum Electronics, 39, 863-879(2022).

    [7] Chen M L, Li E R, Gong W L et al. Ghost imaging lidar via sparsity constraints in real atmosphere[J]. Optics and Photonics Journal, 3, 83-85(2013).

    [87] Minaee S, Boykov Y, Porikli F et al. Image segmentation using deep learning: a survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 3523-3542(2022).

    [109] Liu X, Zhang F J, Hou Z Y et al. Self-supervised learning: generative or contrastive[J]. IEEE Transactions on Knowledge and Data Engineering, 35, 857-876(2023).

    [121] Chen Y F, Sun Z, Li X L. Underwater single-pixel imaging method based on object search and detail enhancement[J]. Acta Photonica Sinica, 53, 0401001(2024).

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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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