Fluorescent light error suppression for high-speed phase-shifting profilometry based on deep learning

Yang Zhao; Nenqing Lv; Haotian Yu; Jing Han; Lianfa Bai; Dongliang Zheng

doi:10.1117/12.2586589

15 January 2021 Fluorescent light error suppression for high-speed phase-shifting profilometry based on deep learning

Yang Zhao, Nenqing Lv, Haotian Yu, Jing Han, Lianfa Bai, Dongliang Zheng

Proceedings Volume 11761, Fourth International Conference on Photonics and Optical Engineering; 1176115 (2021) https://doi.org/10.1117/12.2586589
Event: Fourth International Conference on Photonics and Optical Engineering, 2020, Xi'an, China

Abstract

In the recording process of phase-shifting profilometry, intensity fluctuation caused by uorescent light source instability may occur and then introduce a non-ignorable phase error. More importantly, the selection of sampling speed will also affect the value of the phase error, which even up to 0.12 rad. To suppress this problem, a deep learning-based fluorescent light error suppression (DLFLES) method is proposed to achieve high-precise measurement under fluorescent light. Experiments demonstrate that the shapes of the reconstructed 3-D images are more precise using the proposed method. Our research would promote the development of accurate 3-D measurement under the interference of external light sources by using deep learning.

Citation Download Citation

Yang Zhao, Nenqing Lv, Haotian Yu, Jing Han, Lianfa Bai, and Dongliang Zheng "Fluorescent light error suppression for high-speed phase-shifting profilometry based on deep learning", Proc. SPIE 11761, Fourth International Conference on Photonics and Optical Engineering, 1176115 (15 January 2021); https://doi.org/10.1117/12.2586589

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