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Proceedings Paper

Recovery of phase modulation via residual neural network
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Paper Abstract

An approach for recovering the phase information from the detected intensity was proposed in this work. Unlike the conventional approach based on the Gerchberg-Saxton algorithm, the proposed approach recovered the phase information via an alternative technique in the realm of deep learning, the residual neural network. The database we utilized to train the network was collected by a Michelson-based interferometer, where a spatial light modulator was implemented to provide the phase modulation as the phase object. As the result, the mean absolute error of each pixel was 0.0614π.

Paper Details

Date Published: 12 November 2019
PDF: 3 pages
Proc. SPIE 11197, SPIE Future Sensing Technologies, 111970N (12 November 2019); doi: 10.1117/12.2542620
Show Author Affiliations
Yun-Zhen Yao, National Chiao Tung Univ. (Taiwan)
Jian-Jia Su, National Chiao Tung Univ. (Taiwan)
Jie-En Li, National Chiao Tung Univ. (Taiwan)
Zhi-Yu Zhu, National Chiao Tung Univ. (Taiwan)
Chung-Hao Tien, National Chiao Tung Univ. (Taiwan)


Published in SPIE Proceedings Vol. 11197:
SPIE Future Sensing Technologies
Masafumi Kimata; Christopher R. Valenta, Editor(s)

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