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Bi-frequency temporal phase unwrapping using deep learning
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Paper Abstract

In fringe projection profilometry (FPP), multi-frequency phase unwrapping, as a classical algorithm for temporal phase unwrapping (TPU), can eliminate the phase ambiguities and obtain the unwrapped phase with the aid of additional wrapped phase maps with different fringe periods. However, based on the principle of multi-frequency phase unwrapping, it needs multiple groups of fringe patterns with different fringe periods to eliminate the phase ambiguities of the wrapped phase with high-frequency, which is not suitable for high-speed 3D measurement. If two frequency fringe patterns are only projected, the reliability of multi-frequency phase unwrapping will be decreased significantly. Inspired by deep learning techniques, in this study, we demonstrate that the deep neural networks can learn to perform temporal phase unwrapping after appropriate training, which substantially improves the reliability of phase unwrapping compared with the traditional multi-frequency TPU approach even when high-frequency fringe patterns are used. In our experiment, a challenging problem in TPU is that the unwrapped phase of 64-period fringe patterns cannot be directly unwrapped by only using a single-frequency phase, but it can be easily resolved by our method. Experimental results demonstrate the temporal phase unwrapping method using deep learning provides the best unwrapping reliability to realize the absolute 3D measurement for objects with complex surfaces.

Paper Details

Date Published: 13 May 2019
PDF: 5 pages
Proc. SPIE 10991, Dimensional Optical Metrology and Inspection for Practical Applications VIII, 109910D (13 May 2019); doi: 10.1117/12.2520201
Show Author Affiliations
Wei Yin, Nanjing Univ. of Science and Technology (China)
Chao Zuo, Nanjing Univ. of Science and Technology (China)
Shijie Feng, Nanjing Univ. of Science and Technology (China)
Tianyang Tao, Nanjing Univ. of Science and Technology (China)
Qian Chen, Nanjing Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10991:
Dimensional Optical Metrology and Inspection for Practical Applications VIII
Kevin G. Harding; Song Zhang, Editor(s)

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