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  • Letter
  • Open Access

Statistical dependencies beyond linear correlations in light scattered by disordered media

Ilya Starshynov*, Alex Turpin, Philip Binner, and Daniele Faccio†

  • School of Physics & Astronomy, University of Glasgow, Glasgow G12 8QQ, United Kingdom

  • *ilya.starshynov@glasgow.ac.uk
  • †daniele.faccio@glasgow.ac.uk
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Phys. Rev. Research 4, L022033 – Published 11 May, 2022

DOI: https://doi.org/10.1103/PhysRevResearch.4.L022033

Abstract

Imaging through scattering and random media is an outstanding problem that, to date, has been tackled by either measuring the medium transmission matrix or exploiting linear correlations in the transmitted speckle patterns. However, transmission matrix techniques require interferometric stability and linear correlations, such as the memory effect, can be exploited only in thin scattering media. Here we show the existence of a statistical dependency in strongly scattered optical fields in a case where first-order correlations are not expected. We also show that this statistical dependence and the related information transport is directly linked to artificial neural network imaging in strongly scattering, dynamic media. These nontrivial dependencies provide a key to imaging through dynamic and thick scattering media with applications for deep-tissue imaging or imaging through smoke or fog.

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Outline

Information

Phys. Rev. Research 4, L022033– Published 11 May, 2022
  • Received 12 November 2020
  • Revised 19 September 2021
  • Accepted 30 March 2022
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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