Transmission Matrix Inference via Pseudolikelihood Decimation

TL;DR

This paper introduces a statistical inference method using pseudolikelihood maximization to estimate the transmission matrix in complex media, facilitating advanced biomedical imaging techniques.

Contribution

It presents a novel approach converting the transmission matrix estimation into a statistical problem, leveraging spin-glass theory tools for improved imaging in disordered media.

Findings

Successful inference of transmission matrices in complex media

Enhanced imaging capabilities through statistical modeling

Potential for improved medical imaging techniques

Abstract

One of the biggest challenges in the field of biomedical imaging is the comprehension and the exploitation of the photon scattering through disordered media. Many studies have pursued the solution to this puzzle, achieving light-focusing control or reconstructing images in complex media. In the present work, we investigate how statistical inference helps the calculation of the transmission matrix in a complex scrambling environment, enabling its usage like a normal optical element. We convert a linear input-output transmission problem into a statistical formulation based on pseudolikelihood maximization, learning the coupling matrix via random sampling of intensity realizations. Our aim is to uncover insights from the scattering problem, encouraging the development of novel imaging techniques for better medical investigations, borrowing a number of statistical tools from spin-glass theory.