Opto-Electronic Advances, Volume. 7, Issue 1, 230034(2024)
Physics-informed deep learning for fringe pattern analysis
[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).
[10] Q Kemao. Windowed fourier transform for fringe pattern analysis. Appl Opt, 43, 2695-2702(2004).
[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
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)