Journal
OPTICS EXPRESS
Volume 27, Issue 20, Pages 28929-28943Publisher
OPTICAL SOC AMER
DOI: 10.1364/OE.27.028929
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Funding
- Program for Innovative Research Team in University of Tianjin [TD13-5036]
- Natural Science Foundation of Tianjin City [16JCYBJC15400, 18JCQNJC04400, 18JCQNJC71100]
- National Natural Science Foundation of China [51806150, 60808020, 61078041, 61905178]
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We propose a label enhanced and patch based deep learning phase retrieval approach which can achieve fast and accurate phase retrieval using only several fringe patterns as training dataset. To the best of our knowledge, it is the first time that the advantages of the label enhancement and patch strategy for deep learning based phase retrieval are demonstrated in fringe projection. In the proposed method, the enhanced labeled data in training dataset is designed to learn the mapping between the input fringe pattern and the output enhanced fringe part of the deep neural network (DNN). Moreover, the training data is cropped into small overlapped patches to expand the training samples for the DNN. The performance of the proposed approach is verified by experimental projection fringe patterns with applications in dynamic fringe projection 3D measurement. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
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