Opto-Electronic Advances, Volume. 7, Issue 1, 230034(2024)

Physics-informed deep learning for fringe pattern analysis

Wei Yin1,2,3、†, Yuxuan Che1,2,3、†, Xinsheng Li1,2,3, Mingyu Li1,2,3, Yan Hu1,2,3, Shijie Feng1,2,3、*, Edmund Y. Lam4、**, Qian Chen3、***, and Chao Zuo1,2,3、****
Author Affiliations
  • 1Smart Computational Imaging Laboratory (SCILab), School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
  • 2Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing 210019, China
  • 3Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing 210094, China
  • 4Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR 999077, China
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    References(39)

    [1] KJ Gåsvik. Optical Metrology(2002).

    [4] P Hariharan. Basics of Interferometry(2010).

    [5] U Schnars, C Falldorf, J Watson et al. Digital Holography and Wavefront Sensing(2015).

    [8] M Servin, JA Quiroga, JM Padilla. Fringe Pattern Analysis for Optical Metrology: Theory, Algorithms, and Applications(2014).

    [18] Y Rivenson, YB Zhang, H Günaydın et al. Phase recovery and holographic image reconstruction using deep learning in neural networks. Light Sci Appl, 7, 17141(2018).

    [29] A Saba, C Gigli, AB Ayoub et al. Physics-informed neural networks for diffraction tomography. Adv Photon, 4, 066001(2022).

    [30] GT Reid. Automatic fringe pattern analysis: a review. Opt Lasers Eng, 7, 37-68(1986–1987).

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    Wei Yin, Yuxuan Che, Xinsheng Li, Mingyu Li, Yan Hu, Shijie Feng, Edmund Y. Lam, Qian Chen, Chao Zuo. Physics-informed deep learning for fringe pattern analysis[J]. Opto-Electronic Advances, 2024, 7(1): 230034

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    Paper Information

    Category: Research Articles

    Received: Mar. 7, 2023

    Accepted: May. 12, 2023

    Published Online: Apr. 19, 2024

    The Author Email: Feng Shijie (SJFeng), Lam Edmund Y. (EYLam), Chen Qian (QChen), Zuo Chao (CZuo)

    DOI:10.29026/oea.2024.230034

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