Share Email Print

Proceedings Paper

Fringe pattern filtering using convolutional neural network
Author(s): Ketao Yan; Jiaqi Shi; Tao Sun; Jiaxing Li; Yingjie Yu
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 16 October 2019
PDF: 5 pages
Proc. SPIE 11205, Seventh International Conference on Optical and Photonic Engineering (icOPEN 2019), 112050O (16 October 2019); doi: 10.1117/12.2542401
Show Author Affiliations
Ketao Yan, Shanghai Univ. (China)
Jiaqi Shi, Shanghai Univ. (China)
Tao Sun, Shanghai Univ. (China)
Jiaxing Li, Shanghai Univ. (China)
Yingjie Yu, Shanghai Univ. (China)

Published in SPIE Proceedings Vol. 11205:
Seventh International Conference on Optical and Photonic Engineering (icOPEN 2019)
Anand Asundi; Motoharu Fujigaki; Huimin Xie; Qican Zhang; Song Zhang; Jianguo Zhu; Qian Kemao, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?