(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
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.
Flicker minimization in power-saving displays enabled by measurement of difference in flexoelectric coefficients and displacement-current in positive dielectric anisotropy liquid crystals
Junho Jung, HaYoung Jung, GyuRi Choi, HanByeol Park, Sun-Mi Park, Ki-Sun Kwon, Heui-Seok Jin, Dong-Jin Lee, Hoon Jeong, JeongKi Park, Byeong Koo Kim, Seung Hee Lee, MinSu Kim
Opto-Electronic Advances
2025-09-25
Dual-frequency angular-multiplexed fringe projection profilometry with deep learning: breaking hardware limits for ultra-high-speed 3D imaging
Wenwu Chen, Yifan Liu, Shijie Feng, Wei Yin, Jiaming Qian, Yixuan Li, Hang Zhang, Maciej Trusiak, Malgorzata Kujawinska, Qian Chen, Chao Zuo
Opto-Electronic Advances
2025-09-25
