Image Transmission Through a Dynamically Perturbed Multimode Fiber by Deep Learning

Shachar Resisi, Sebastien M. Popoff, Yaron Bromberg*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

51 Scopus citations

Abstract

When multimode optical fibers are perturbed, the data that is transmitted through them is scrambled. This presents a major difficulty for many possible applications, such as multimode fiber based telecommunication and endoscopy. To overcome this challenge, a deep learning approach that generalizes over mechanical perturbations is presented. Using this approach, successful reconstruction of the input images from intensity-only measurements of speckle patterns at the output of a 1.5 m-long randomly perturbed multimode fiber is demonstrated. The model's success is explained by hidden correlations in the speckle of random fiber conformations.

Original languageAmerican English
Article number2000553
JournalLaser and Photonics Reviews
Volume15
Issue number10
DOIs
StatePublished - Oct 2021

Bibliographical note

Publisher Copyright:
© 2021 Wiley-VCH GmbH

Keywords

  • deep learning
  • endoscopy
  • image reconstruction
  • imaging
  • multimode optical fibers
  • speckle

Fingerprint

Dive into the research topics of 'Image Transmission Through a Dynamically Perturbed Multimode Fiber by Deep Learning'. Together they form a unique fingerprint.

Cite this