|
Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Engineering / Faculty of Engineering >
Peer-reviewed Journal Articles, etc >
Phase Extraction from Single Interferogram Including Closed-Fringe Using Deep Learning
This item is licensed under:Creative Commons Attribution 4.0 International
| 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)
|
|
