Recovery of phase modulation via residual neural network

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationSPIE Future Sensing Technologies
EditorsMasafumi Kimata, Christopher R. Valenta
PublisherSPIE
ISBN (Electronic)9781510631113
DOIs
StatePublished - 12 Nov 2019
EventSPIE Future Sensing Technologies 2019 - Tokyo, Japan
Duration: 14 Nov 2019 → …

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11197
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceSPIE Future Sensing Technologies 2019
CountryJapan
CityTokyo
Period14/11/19 → …

Keywords

  • Deep learning
  • Digital imaging processing
  • Optical imaging
  • Phase retrieval
  • Spatial light modulator

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