Gamma correction by using deep learning

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.