@inproceedings{a74ee94e904041249a1eca8c5b45b29a,
title = "Recovery of phase modulation via residual neural network",
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.",
keywords = "Deep learning, Digital imaging processing, Optical imaging, Phase retrieval, Spatial light modulator",
author = "Yao, {Yun Zhen} and Su, {Jian Jia} and Li, {Jie En} and Zhu, {Zhi Yu} and Tien, {Chung Hao}",
note = "Publisher Copyright: Copyright {\textcopyright} 2019 SPIE.; SPIE Future Sensing Technologies 2019 ; Conference date: 14-11-2019",
year = "2019",
month = nov,
day = "12",
doi = "10.1117/12.2542620",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Masafumi Kimata and Valenta, {Christopher R.}",
booktitle = "SPIE Future Sensing Technologies",
address = "美國",
}