Imaging with super-resolution in changing random media
This work addresses imaging challenges in complex, dynamic environments like medical or geophysical sensing, but it appears incremental as it builds on existing sparse dictionary learning and clustering techniques.
The paper tackles the problem of achieving super-resolution imaging in changing random media by exploiting strong scattering, and the result is an algorithm that reliably extracts unknown medium properties to enable super-resolution beyond homogeneous medium limits when abundant data are available.
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.