A learning based on approach for noise reduction with raster images

被引：0
|
作者：
Wang J. [1 ,2 ,3 ]
Li Y. [1 ,2 ,3 ]
Zhang Y. [1 ,2 ,3 ]
机构：
[1] School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing
[2] Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing
[3] Smart Computational Imaging Laboratory (SCILab), Nanjing University of Science and Technology, Nanjing
来源：
Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering | 2022年 / 51卷 / 02期
关键词：
Deep learning; High-speed 3D shape measurement; Noisy fringe images; Phase recovery;
D O I：
10.3788/IRLA20220006
中图分类号：
学科分类号：
摘要：
Three-dimensional (3D) shape measurement based on fringe projection was widely used in industrial manufacturing, quality testing, biomedicine, aerospace and other fields. However, due to the short exposure time of raster images acquisition process, 3D reconstruction results were usually affected by serious image noise in the scene of high-speed measurement. In recent years, deep learning has been widely used in computer vision and other fields, and has achieved great success. Inspired by this, we proposed a learning based approach for noise reduction with raster images. Firstly, we constructed a convolutional neural network based on U-NET. Secondly, the neural network was constructed to learn the mapping relationship between the noisy fringe images and the corresponding high quality wrapped phase during the training process. With proper training, this network can accurately recovered phase information from noisy fringe images. Aiming at off-line 3D measurement in fast moving scene, experimental results show that the proposed method can recover high-precision phase information by using only one raster image, and the phase accuracy is better than the traditional three-step phase shift method. This method can provide a practical and reliable solution for improving the accuracy of 3D measurement in high-speed scene. Copyright ©2022 Infrared and Laser Engineering. All rights reserved.
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