Imaging with super-resolution in changing random media

TL;DR

This paper presents a novel imaging algorithm that leverages strong scattering in changing random media to achieve super-resolution, utilizing large datasets and advanced processing techniques like sparse dictionary learning and clustering.

Contribution

The authors introduce a new method that exploits scattering to surpass traditional resolution limits in imaging within changing random media.

Findings

Scattering enhances imaging resolution beyond homogeneous medium limits.

The algorithm reliably extracts medium properties for accurate imaging.

Super-resolution is achievable with abundant data.

Abstract

We develop an imaging algorithm that exploits strong scattering to achieve super-resolution in changing random media. The method processes large and diverse array datasets using sparse dictionary learning, clustering, and multidimensional scaling. Starting from random initializations, the algorithm reliably extracts the unknown medium properties necessary for accurate imaging using back-propagation, $\ell_2$ or $\ell_1$ methods. Remarkably, scattering enhances resolution beyond homogeneous medium limits. When abundant data are available, the algorithm allows the realization of super-resolution in imaging.