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Phase Extraction from Single Interferogram Including Closed-Fringe Using Deep Learning

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Title: Phase Extraction from Single Interferogram Including Closed-Fringe Using Deep Learning
Authors: Kando, Daichi Browse this author
Tomioka, Satoshi Browse this author →KAKEN DB
Miyamoto, Naoki Browse this author →KAKEN DB
Ueda, Ryosuke Browse this author
Keywords: deep learning
convolutional network
U-net
phase extraction
fringe analysis
closed-fringe
interferometer
Issue Date: 1-Sep-2019
Publisher: MDPI
Journal Title: Applied sciences
Volume: 9
Issue: 17
Start Page: 3529
Publisher DOI: 10.3390/app9173529
Abstract: In an optical measurement system using an interferometer, a phase extracting technique from interferogram is the key issue. When the object is varying in time, the Fourier-transform method is commonly used since this method can extract a phase image from a single interferogram. However, there is a limitation, that an interferogram including closed-fringes cannot be applied. The closed-fringes appear when intervals of the background fringes are long. In some experimental setups, which need to change the alignments of optical components such as a 3-D optical tomographic system, the interval of the fringes cannot be controlled. To extract the phase from the interferogram including the closed-fringes we propose the use of deep learning. A large amount of the pairs of the interferograms and phase-shift images are prepared, and the trained network, the input for which is an interferogram and the output a corresponding phase-shift image, is obtained using supervised learning. From comparisons of the extracted phase, we can demonstrate that the accuracy of the trained network is superior to that of the Fourier-transform method. Furthermore, the trained network can be applicable to the interferogram including the closed-fringes, which is impossible with the Fourier transform method.
Rights: © 2019 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 License (http://creativecommons.org/licenses/by/4.0/).
http://creativecommons.org/licenses/by/4.0/
Type: article
URI: http://hdl.handle.net/2115/76014
Appears in Collections:工学院・工学研究院 (Graduate School of Engineering / Faculty of Engineering) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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