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Generalized framework for non-sinusoidal fringe analysis using deep learning
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PRE
AI
DOI：10.1364/PRJ.420944.png)
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摘要
En 中文
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. (C) 2021 Chinese Laser Press
Keyword:
3D SHAPE MEASUREMENT
GAMMA-CORRECTION
ERROR
SATURATION
期刊
IF:
7.2
论文数:
2.4K
被引数:
1.3W
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