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

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 proc...

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Detalles Bibliográficos
Autores principales: Liang, Jian, Zhang, Junchao, Shao, Jianbo, Song, Bofan, Yao, Baoli, Liang, Rongguang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7374453/
https://www.ncbi.nlm.nih.gov/pubmed/32630246
http://dx.doi.org/10.3390/s20133691
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author Liang, Jian
Zhang, Junchao
Shao, Jianbo
Song, Bofan
Yao, Baoli
Liang, Rongguang
author_facet Liang, Jian
Zhang, Junchao
Shao, Jianbo
Song, Bofan
Yao, Baoli
Liang, Rongguang
author_sort Liang, Jian
collection PubMed
description 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.
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spelling pubmed-73744532020-08-06 Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging Liang, Jian Zhang, Junchao Shao, Jianbo Song, Bofan Yao, Baoli Liang, Rongguang Sensors (Basel) Article 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. MDPI 2020-07-01 /pmc/articles/PMC7374453/ /pubmed/32630246 http://dx.doi.org/10.3390/s20133691 Text en © 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/).
spellingShingle Article
Liang, Jian
Zhang, Junchao
Shao, Jianbo
Song, Bofan
Yao, Baoli
Liang, Rongguang
Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging
title Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging
title_full Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging
title_fullStr Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging
title_full_unstemmed Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging
title_short Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging
title_sort deep convolutional neural network phase unwrapping for fringe projection 3d imaging
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7374453/
https://www.ncbi.nlm.nih.gov/pubmed/32630246
http://dx.doi.org/10.3390/s20133691
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