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Open AccessArticle

Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging

1
State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China
2
James C. Wyant College of Optical Sciences, University of Arizona, Tucson, AZ 85721, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(13), 3691; https://doi.org/10.3390/s20133691
Received: 25 May 2020 / Revised: 26 June 2020 / Accepted: 29 June 2020 / Published: 1 July 2020
(This article belongs to the Section Optical Sensors)
Phase unwrapping is a very important step in fringe projection 3D imaging. In this paper, we propose a new neural network for accurate phase unwrapping to address the special needs in fringe projection 3D imaging. Instead of labeling the wrapped phase with integers directly, a two-step training process with the same network configuration is proposed. In the first step, the network (network I) is trained to label only four key features in the wrapped phase. In the second step, another network with same configuration (network II) is trained to label the wrapped phase segments. The advantages are that the dimension of the wrapped phase can be much larger from that of the training data, and the phase with serious Gaussian noise can be correctly unwrapped. We demonstrate the performance and key features of the neural network trained with the simulation data for the experimental data. View Full-Text
Keywords: phase unwrapping; deep learning; 3D imaging phase unwrapping; deep learning; 3D imaging
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Liang, J.; Zhang, J.; Shao, J.; Song, B.; Yao, B.; Liang, R. Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging. Sensors 2020, 20, 3691.

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