Deep learning based method for phase analysis from a single closed fringe pattern

Peihang Li; Ming-Feng Lu; Jinmin Wu; Chenchen Ji; Gang Yu; Ran Tao

doi:10.1117/12.2574765

15 June 2020 Deep learning based method for phase analysis from a single closed fringe pattern

Peihang Li, Ming-Feng Lu, Jinmin Wu, Chenchen Ji, Gang Yu, Ran Tao

Proceedings Volume 11523, Optical Technology and Measurement for Industrial Applications 2020; 115230E (2020) https://doi.org/10.1117/12.2574765
Event: SPIE Technologies and Applications of Structured Light, 2020, Yokohama, Japan

Abstract

Interferometry is a widely used optical measurement technique. We can estimate the physical parameters of the measured object by analyzing the phase of the fringe pattern obtained by interference imaging. However, when the measurement object has spherical surface, the interferogram always contains closed fringes which the traditional analysis methods are difficult to handle. Therefore, we use several common deep learning networks to learn the closed fringe patterns and their phases, evaluate and choose the appropriate network to build an end-to-end phase analysis system for a single closed fringe pattern. The experimental results show that the constructed deep learning network model has excellent phase recovery effect on simulation closed fringe patterns, and can estimate the curvature radius of the spherical surface accurately.

Citation Download Citation

Peihang Li, Ming-Feng Lu, Jinmin Wu, Chenchen Ji, Gang Yu, and Ran Tao "Deep learning based method for phase analysis from a single closed fringe pattern", Proc. SPIE 11523, Optical Technology and Measurement for Industrial Applications 2020, 115230E (15 June 2020); https://doi.org/10.1117/12.2574765

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