Fringe pattern filtering using convolutional neural network

Abstract

Fringe pattern denoising is an important process for fringe pattern analysis. In this paper, fringe pattern denoising using the convolutional neural network (CNN) is introduced. We use Gaussian functions to generate the various phase distributions, and then the required training samples are simulated according to theoretical formulas. The noisy fringe pattern can directly obtain the clean fringe pattern using the trained model. The denoising performance has been verified, which can recover high-quality fringe pattern.