Physics-informed deep learning for fringe pattern analysis

Physics-informed deep learning for fringe pattern analysis

Recently, deep learning has yielded transformative success across optics and photonics, especially in optical metrology. Deep neural networks (DNNs) with a fully convolutional architecture (e.g., U-Net and its derivatives) have been widely implemented in an end-to-end manner to accomplish various op...

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Bibliographic Details
Main Authors: Wei Yin, Yuxuan Che, Xinsheng Li, Mingyu Li, Yan Hu, Shijie Feng, Edmund Y. Lam, Qian Chen, Chao Zuo
Format: Article
Language:English
Published: Institue of Optics and Electronics, Chinese Academy of Sciences 2024-01-01
Series:Opto-Electronic Advances
Subjects:
optical metrology
deep learning
physics-informed neural networks
fringe analysis
phase retrieval
Online Access:https://www.oejournal.org/article/doi/10.29026/oea.2024.230034

Internet

https://www.oejournal.org/article/doi/10.29026/oea.2024.230034

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