Super-resolution in disordered media using neural networks

TL;DR

This paper introduces a neural network-based method to achieve super-resolution imaging in strongly scattering media by estimating Green's functions from large datasets, surpassing the resolution of homogeneous media.

Contribution

The work demonstrates a novel approach combining neural networks and data-driven Green's function estimation to enhance imaging resolution in complex scattering environments.

Findings

Achieved super-resolution imaging in disordered media

Neural networks improve Green's function estimation accuracy

Enhanced physical imaging aperture through scattering

Abstract

We propose a methodology that exploits large and diverse data sets to accurately estimate the ambient medium's Green's functions in strongly scattering media. Given these estimates, obtained with and without the use of neural networks, excellent imaging results are achieved, with a resolution that is better than that of a homogeneous medium. This phenomenon, also known as super-resolution, occurs because the ambient scattering medium effectively enhances the physical imaging aperture. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.