Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging
Affiliation
Univ Arizona, James C Wyant Coll Opt SciIssue Date
2020-07
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MDPICitation
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.Journal
SENSORSRights
Copyright © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
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.Note
Open access journalISSN
1424-8220EISSN
1424-8220PubMed ID
32630246Version
Final published versionae974a485f413a2113503eed53cd6c53
10.3390/s20133691
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Except where otherwise noted, this item's license is described as Copyright © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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