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Deep neural network-based phase-recovery and auto- focusing extend the depth-of-field in digital holography

  • Yichen Wu
  • , Yair Rivenson
  • , Yibo Zhang
  • , Zhensong Wei
  • , Harun Gunaydin
  • , Xing Lin
  • , Aydogan Ozcan

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

Abstract

A deep convolutional neural network simultaneously performs auto-focusing and phase-recovery using a single hologram intensity, and achieves > 25-fold and > 30-fold increase in depth-of-field and reconstruction speed of digital holographic imaging, respectively.

Original languageEnglish
Title of host publicationDigital Holography and Three-Dimensional Imaging, DH 2018
PublisherOptica Publishing Group (formerly OSA)
ISBN (Print)9781943580446
DOIs
StatePublished - 1 Jan 2018
Externally publishedYes
EventDigital Holography and Three-Dimensional Imaging, DH 2018 - Orlando, United States
Duration: 25 Jun 201828 Jun 2018

Publication series

NameOptics InfoBase Conference Papers
VolumePart F100-DH 2018
ISSN (Electronic)2162-2701

Conference

ConferenceDigital Holography and Three-Dimensional Imaging, DH 2018
Country/TerritoryUnited States
CityOrlando
Period25/06/1828/06/18

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Mechanics of Materials

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