Paper
7 October 2020 Single-shot spatial frequency multiplex fringe pattern for phase unwrapping using deep learning
Author Affiliations +
Proceedings Volume 11571, Optics Frontier Online 2020: Optics Imaging and Display; 1157118 (2020) https://doi.org/10.1117/12.2580642
Event: Optics Frontiers Online 2020: Optics Imaging and Display (OFO-1), 2020, Shanghai, China
Abstract
Fringe projection profilometry (FPP) has been widely applied in three-dimensional (3D) measurement owing to its high measurement accuracy and simple structure. In FPP, how to effectively recover the absolute phase, especially through a single image, has always been a huge challenge and eternal pursuit. The frequency-multiplex methods can maximize the efficiency of phase unwrapping by mixing the multi-frequency information used to eliminate phase ambiguity in the spectrum. However, spectrum aliasing and the resulting phase unwrapping errors are still pressing difficulties. Inspired by the successful application of deep learning in FPP, we propose a single-shot frequency multiplex fringe pattern for phase unwrapping approach using deep learning. Through extensive data learning, the properly trained neural networks can directly learn to obtain spectrum-aliasingfree phase information and robust phase unwrapping from single-frame compound input. Experimental results demonstrate that compared with convenient frequency-multiplex methods, our deep-learning-based approach can achieve more accurate and stable absolute phase retrieval.
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Yixuan Li, Jiaming Qian, Shijie Feng, Qian Chen, and Chao Zuo "Single-shot spatial frequency multiplex fringe pattern for phase unwrapping using deep learning", Proc. SPIE 11571, Optics Frontier Online 2020: Optics Imaging and Display, 1157118 (7 October 2020); https://doi.org/10.1117/12.2580642
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KEYWORDS
Fringe analysis

Composites

3D metrology

Spatial frequencies

3D modeling

Cameras

Image quality

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