# Imaging with super-resolution in changing random media

arXiv: https://arxiv.org/abs/2511.14147 · HTML: https://mlsanity.com/paper/2511.14147/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 18 Nov 2025.

## Builds on (most cited first, 3 of 3)

- [A note on the sample complexity of the Er-SpUD algorithm by Spielman, Wang and Wright for exact recovery of sparsely used dictionaries](https://mlsanity.com/paper/1601.02049/) (2016)
- [A Clustering Approach to Learn Sparsely-Used Overcomplete Dictionaries](https://mlsanity.com/paper/1309.1952/) (2013)
- [Dictionary Learning for the Almost-Linear Sparsity Regime](https://mlsanity.com/paper/2210.10855/) (2022)

## Built on by (most cited first, 1 of 1)

- [Iterative Atom Refinement: A Monotonicity Principle for Dictionary Learning](https://mlsanity.com/paper/2609.23812/) (2026)

## Abstract (arXiv)

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, ℓ2 or ℓ1 methods. Remarkably, scattering enhances resolution beyond homogeneous medium limits. When abundant data are available, the algorithm allows the realization of super-resolution in imaging.