Optical pixel-to-plane encoding with neural network for ghost transmission through complex scattering media

Yang Peng, Wen Chen

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

Abstract

Ghost transmission through scattering media remains an open question. Here, we present high-fidelity ghost transmission through complex scattering media using random patterns as information carriers. Pixel values of an analog signal to be transmitted are sequentially encoded into random patterns by employing the untrained neural network (UNN). In optical experiments, the laser beam illuminates the designed random patterns and passes through complex scattering media. The light intensities recorded by a single-pixel detector are used to retrieve the encoded analog signal. Experimental results demonstrate that the developed pixel-to-plane pattern encoding can achieve high-quality ghost transmission through complex scattering media. The method provides a solution for ghost transmission in complex environments by applying UNN for optical encoding.

Original languageEnglish
Title of host publicationInternational Conference on Optical and Photonic Engineering, icOPEN 2023
EditorsHaixia Wang, Fang Cheng, Cuong Dang, Aaron Danner, Qian Kemao
PublisherSPIE
ISBN (Electronic)9781510674561
DOIs
Publication statusPublished - Feb 2024
Event2023 International Conference on Optical and Photonic Engineering, icOPEN 2023 - Singapore, Singapore
Duration: 27 Nov 20231 Dec 2023

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume13069
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference2023 International Conference on Optical and Photonic Engineering, icOPEN 2023
Country/TerritorySingapore
CitySingapore
Period27/11/231/12/23

Keywords

  • complex environments
  • Ghost transmission
  • information carriers
  • pixel-to-plane pattern encoding
  • untrained neural network

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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