Imaging through Turbulent Media Using Deep Learning Method

Lina Zhou, Xudong Chen, Wen Chen

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

We present deep learning method that can be used to reconstruct high-quality objects through turbulent media mixed with water and milk. The objects are placed behind turbulent media, and a series of speckle patterns are correspondingly recorded. By using many pairs of the recorded speckle patterns and input object images, a designed convolutional neural network (CNN) is fully trained, and then enables the recorded speckle patterns to be processed in real time. The proposed method is promising for imaging through turbulent media, and it is also believed that the proposed method can be applicable in many areas, e.g., imaging and information optics (such as optical encoding).

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 18th International Conference on Industrial Informatics, INDIN 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages521-524
Number of pages4
ISBN (Electronic)9781728149646
DOIs
Publication statusPublished - 20 Jul 2020
Event18th IEEE International Conference on Industrial Informatics, INDIN 2020 - Virtual, Warwick, United Kingdom
Duration: 21 Jul 202023 Jul 2020

Publication series

NameIEEE International Conference on Industrial Informatics (INDIN)
Volume2020-July
ISSN (Print)1935-4576

Conference

Conference18th IEEE International Conference on Industrial Informatics, INDIN 2020
Country/TerritoryUnited Kingdom
CityVirtual, Warwick
Period21/07/2023/07/20

Keywords

  • deep learning method
  • optical imaging
  • turbulent media

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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