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[2511.14147] Imaging with super-resolution in changing random media
Summary
This article presents a novel imaging algorithm that utilizes strong scattering to achieve super-resolution in dynamic random media, enhancing resolution beyond traditional limits.
Why It Matters
The development of this imaging algorithm is significant as it addresses challenges in capturing high-resolution images in environments with changing random media. This has implications for various fields, including optics and machine learning, where accurate imaging is crucial for advancements in technology and research.
Key Takeaways
- The algorithm leverages strong scattering to improve imaging resolution.
- It employs sparse dictionary learning and clustering for data processing.
- The method can extract unknown medium properties effectively.
- Super-resolution is achievable even in dynamic environments.
- Abundant data availability enhances the algorithm's performance.
Physics > Optics arXiv:2511.14147 (physics) [Submitted on 18 Nov 2025 (v1), last revised 17 Feb 2026 (this version, v2)] Title:Imaging with super-resolution in changing random media Authors:Alexander Christie, Matan Leibovich, Miguel Moscoso, Alexei Novikov, George Papanicolaou, Chrysoula Tsogka View a PDF of the paper titled Imaging with super-resolution in changing random media, by Alexander Christie and 4 other authors View PDF HTML (experimental) 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. Subjects: Optics (physics.optics); Machine Learning (cs.LG) Cite as: arXiv:2511.14147 [physics.optics] (or arXiv:2511.14147v2 [physics.optics] for this version) https://doi.org/10.48550/arXiv.2511.14147 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Chrysoula Tsogka [view email] [v1] Tue, 18 Nov 2025 05:18:00 UTC (816 KB) [v2] Tue, 17 Feb 2026 22:14:33 UTC (816 KB) Full-text links: Acc...
