Volume 16 Issue 3
May  2023
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Chinese Optics  > 2023  >  16(3): 479-499.
ZHAO Lu-da, DONG Xiao, XU Shi-long, HU Yi-hua, ZHANG Xin-yuan, ZHONG Yi-cheng. Recent progress of non-line-of-sight imaging reconstruction algorithms in typical imaging modalities[J]. Chinese Optics, 2023, 16(3): 479-499. doi: 10.37188/CO.2022-0186
Citation: ZHAO Lu-da, DONG Xiao, XU Shi-long, HU Yi-hua, ZHANG Xin-yuan, ZHONG Yi-cheng. Recent progress of non-line-of-sight imaging reconstruction algorithms in typical imaging modalities[J]. Chinese Optics, 2023, 16(3): 479-499. doi: 10.37188/CO.2022-0186
shu

Recent progress of non-line-of-sight imaging reconstruction algorithms in typical imaging modalities

  • 1.

    College of Electronic Engineering, National University of Defence Technology, Hefei 230037, China

  • 2.

    State Key Laboratory of Pulsed Power Laser Technology, National University of Defense Technology, Hefei 230037, China

  • 3.

    Anhui Province Key Laboratory of Electronic Restriction, National University of Defense Technology, Hefei 230037, China

  • 4.

    Unit 77126 of the PLA, Kaiyuan 661600, China

Funds:  Supported by National Natural Science Foundation of China (No. 61871389, No.62201597); the Research Plan Project of the National University of Defense Technology (No. ZK18-01-02, No. ZK22-35); Independent Scientific Research Project of National University of Defense Technology (No. 22-ZZCX-07); National Defense Science and Technology Innovation Special Zone Project (No. 22-TQ23-07-ZD-01-001); Military Graduate Student Funding Priorities (No. JY2021B042); Graduate Research Innovation Project of Hunan Province (No. CX20220045)
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    Abstract

  • Non-line-of-sight (NLoS) imaging is a promising technique developed in recent years, which can reconstruct hidden scenes by analyzing the information in the intermediate surface, and "see around the corner", and has strong application value in many fields. In this paper, we review the reconstruction algorithm for NLoS imaging tasks. Firstly, considering the crossover and non-independent phenomena existing in the NLoS imaging classification, we use the different features of physical imaging models and algorithm models to reclassify them. Secondly, according to the proposed classification criteria, we respectively review the traditional and deep learning-based NLoS imaging reconstruction algorithms, summarize the state-of-the-art algorithms, and derive the implement principle. We also compare the results of deep learning-based and traditional NLoS imaging reconstruction algorithms for reconstruction tasks. Finally, the current challenges and the future development of NLoS imaging are summarized. Different types of NLoS imaging reconstruction algorithms are comprehensively analyzed in this review, which provides important support for the further development of NLoS imaging reconstruction algorithms.

     

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