Paper
7 October 2020 Gamma correction by using deep learning
Author Affiliations +
Proceedings Volume 11571, Optics Frontier Online 2020: Optics Imaging and Display; 115710V (2020) https://doi.org/10.1117/12.2580391
Event: Optics Frontiers Online 2020: Optics Imaging and Display (OFO-1), 2020, Shanghai, China
Abstract
Fringe projection profilometry (FPP) is one of the most important optical non-contact three-dimensional (3D) measurement technologies. However, in order to satisfy human beings’ visual perception, gamma is artificially added to the digital projector. In past decades, researchers have made efforts to compensate gamma nonlinear errors, but how to efficiently and conveniently correct the gamma distortion is still a big challenge. Inspired by the successful application of deep learning in FPP, we propose a deep-learning-based gamma compensation method. Through extensive data set training, the neural network can learn to acquire the distortion-free high-quality phase information from the phase-shifting images with gamma. Experimental results demonstrate that our method can effectively compensate gamma-induced phase errors, and thus improve the measurement accuracy.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
ShuaiJie Wu and Yuzhen Zhang "Gamma correction by using deep learning", Proc. SPIE 11571, Optics Frontier Online 2020: Optics Imaging and Display, 115710V (7 October 2020); https://doi.org/10.1117/12.2580391
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KEYWORDS
Phase shifts

3D modeling

Error analysis

Fringe analysis

Neural networks

Projection systems

Distortion

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