Recovery of phase modulation via residual neural network

Yun-Zhen Yao; Jian-Jia Su; Jie-En Li; Zhi-Yu Zhu; Chung-Hao Tien

doi:10.1117/12.2542620

12 November 2019 Recovery of phase modulation via residual neural network

Yun-Zhen Yao, Jian-Jia Su, Jie-En Li, Zhi-Yu Zhu, Chung-Hao Tien

Proceedings Volume 11197, SPIE Future Sensing Technologies; 111970N (2019) https://doi.org/10.1117/12.2542620
Event: SPIE Future Sensing Technologies, 2019, Tokyo, Japan

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.

Citation Download Citation

Yun-Zhen Yao, Jian-Jia Su, Jie-En Li, Zhi-Yu Zhu, and Chung-Hao Tien "Recovery of phase modulation via residual neural network", Proc. SPIE 11197, SPIE Future Sensing Technologies, 111970N (12 November 2019); https://doi.org/10.1117/12.2542620

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