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(Peer-Reviewed) Deep learning enabled single-shot absolute phase recovery in high-speed composite fringe pattern profilometry of separated objects
Maciej Trusiak, Malgorzata Kujawinska
Institute of Micromechanics and Photonics, Warsaw University of Technology, 8 Sw. A. Boboli Street, Warsaw 02-525, Poland
Opto-Electronic Advances, 2023-12-12
https://doi.org/10.29026/oea.2023.230172
https://www.oejournal.org/article/doi/10.29026/oea.2023.230172
Abstract

A recent article in the Opto-Electronic Advances (OEA) journal from Prof. Qian Chen and Prof. Chao Zuo’s group introduced a new and efficient 3D imaging system that captures high-speed images using deep learning-enabled fringe projection profilometry (FPP).

In this News & Views article, we explore potential avenues for future advancements, including expanding the measurement range through an extended number-theoretical approach, enhancing quality through the incorporation of horizontal fringes, and integrating data from other modalities to broaden the system's applications.
Deep learning enabled single-shot absolute phase recovery in high-speed composite fringe pattern profilometry of separated objects_1
Deep learning enabled single-shot absolute phase recovery in high-speed composite fringe pattern profilometry of separated objects_2
Deep learning enabled single-shot absolute phase recovery in high-speed composite fringe pattern profilometry of separated objects_3
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