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Enhancing resolution in coherent microscopy using deep learning

  • Tairan Liu
  • , Kevin De Haan
  • , Yair Rivenson
  • , Zhensong Wei
  • , Xin Zeng
  • , Yibo Zhang
  • , Aydogan Ozcan

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

Abstract

A generative adversarial network (GAN) based super-resolution framework is presented. This deep learning-based framework is capable of enhancing the resolution of coherent imaging systems in both pixel size-limited and diffraction-limited microscopy systems.

Original languageEnglish
Title of host publicationCLEO
Subtitle of host publicationApplications and Technology, CLEO_AT 2019
PublisherOptica Publishing Group (formerly OSA)
ISBN (Print)9781943580576
DOIs
StatePublished - 1 Jan 2019
Externally publishedYes
EventCLEO: Applications and Technology, CLEO_AT 2019 - San Jose, United States
Duration: 5 May 201910 May 2019

Publication series

NameOptics InfoBase Conference Papers
VolumePart F127-CLEO_AT 2019
ISSN (Electronic)2162-2701

Conference

ConferenceCLEO: Applications and Technology, CLEO_AT 2019
Country/TerritoryUnited States
CitySan Jose
Period5/05/1910/05/19

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Mechanics of Materials

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