Phase Recovery and Holographic Imaging using Recurrent Neural Networks (RNNs)

  • Luzhe Huang
  • , Tairan Liu
  • , Xilin Yang
  • , Yi Luo
  • , Yair Rivenson
  • , Aydogan Ozcan

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

Abstract

We demonstrate a recurrent neural network (RNN) enabled holographic imaging method that simultaneously performs autofocusing and phase recovery, achieving faster reconstruction speed and extended depth-of-field.

Original languageEnglish
Title of host publication2022 Conference on Lasers and Electro-Optics, CLEO 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)9781957171050
StatePublished - 1 Jan 2022
Externally publishedYes
Event2022 Conference on Lasers and Electro-Optics, CLEO 2022 - San Jose, United States
Duration: 15 May 202220 May 2022

Publication series

Name2022 Conference on Lasers and Electro-Optics, CLEO 2022 - Proceedings

Conference

Conference2022 Conference on Lasers and Electro-Optics, CLEO 2022
Country/TerritoryUnited States
CitySan Jose
Period15/05/2220/05/22

ASJC Scopus subject areas

  • Instrumentation
  • Spectroscopy
  • Biomedical Engineering
  • Electrical and Electronic Engineering
  • Management, Monitoring, Policy and Law
  • Materials Science (miscellaneous)
  • Acoustics and Ultrasonics
  • Atomic and Molecular Physics, and Optics

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