Open AccessLabel enhanced and patch based deep learning for phase retrieval from single frame fringe pattern in fringe projection 3D measurement
Author(s) -
Shi Jin,
Xinjun Zhu,
Hongyi Wang,
Limei Song,
Qinghua Guo
Publication year - 2019
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.27.028929
Subject(s) - computer science , phase retrieval , structured light 3d scanner , artificial intelligence , projection (relational algebra) , frame (networking) , artificial neural network , pattern recognition (psychology) , phase (matter) , deep learning , computer vision , optics , fourier transform , algorithm , mathematics , physics , mathematical analysis , telecommunications , scanner , quantum mechanics
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.