镜面反射
光学
计算机科学
高动态范围
相(物质)
帧(网络)
相位恢复
镜面反射高光
人工智能
动态范围
计算机视觉
物理
傅里叶变换
电信
量子力学
作者
Zhaoxing Wu,Jie Wang,Xuan Jiang,Luyao Fan,Wei Chen,Huimin Yue,Yong Liu
出处
期刊:Optics Express
[Optica Publishing Group]
日期:2023-04-28
卷期号:31 (11): 17437-17437
被引量:8
摘要
In order to solve the difficulty of traditional phase measuring deflectometry (PMD) in considering precision and speed, an orthogonal encoding PMD method based on deep learning is presented in this paper. We demonstrate for, what we believe to be, the first time that deep learning techniques can be combined with dynamic-PMD and can be used to reconstruct high-precision 3D shapes of specular surfaces from single-frame distorted orthogonal fringe patterns, enabling high-quality dynamic measurement of specular objects. The experimental results prove that the phase and shape information measured by the proposed method has high accuracy, almost reaching the results obtained by the ten-step phase-shifting method. And the proposed method also has excellent performance in dynamic experiments, which is of great significance to the development of optical measurement and fabrication areas.
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