计算机科学
结构光三维扫描仪
人工智能
人工神经网络
投影(关系代数)
光学
相位恢复
计量学
谐波
深度学习
计算机视觉
模式识别(心理学)
算法
傅里叶变换
物理
扫描仪
量子力学
电压
作者
Shijie Feng,Chao Zuo,Liang Zhang,Wei Yin,Qian Chen
出处
期刊:Photonics Research
[The Optical Society]
日期:2021-05-27
卷期号:9 (6): 1084-1084
被引量:70
摘要
Phase retrieval from fringe images is essential to many optical metrology applications. In the field of fringe projection profilometry, the phase is often obtained with systematic errors if the fringe pattern is not a perfect sinusoid. Several factors can account for non-sinusoidal fringe patterns, such as the non-linear input–output response (e.g., the gamma effect) of digital projectors, the residual harmonics in binary defocusing projection, and the image saturation due to intense reflection. Traditionally, these problems are handled separately with different well-designed methods, which can be seen as “one-to-one” strategies. Inspired by recent successful artificial intelligence-based optical imaging applications, we propose a “one-to-many” deep learning technique that can analyze non-sinusoidal fringe images resulting from different non-sinusoidal factors and even the coupling of these factors. We show for the first time, to the best of our knowledge, a trained deep neural network can effectively suppress the phase errors due to various kinds of non-sinusoidal patterns. Our work paves the way to robust and powerful learning-based fringe analysis approaches.
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