Statistical Dependencies Beyond Linear Correlations in Light Scattered by Disordered Media

TL;DR

This paper uncovers complex statistical dependencies in strongly scattered optical fields that are not explained by linear correlations, enabling improved imaging through dynamic, thick scattering media such as biological tissue or fog.

Contribution

It reveals non-linear statistical dependencies in scattered light and links them to neural network imaging, advancing imaging techniques in challenging scattering environments.

Findings

Identified statistical dependencies beyond linear correlations in scattered light.

Linked these dependencies to neural network-based imaging in dynamic media.

Demonstrated potential for improved imaging through thick, dynamic scattering media.

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 non-trivial dependencies provide a key to imaging through dynamic and thick scattering media with applications for deep-tissue imaging or imaging through smoke or fog