Super-resolution in disordered media using neural networks
Machine Learning
2024-12-05 v4 Computer Vision and Pattern Recognition
Image and Video Processing
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.
Cite
@article{arxiv.2410.21556,
title = {Super-resolution in disordered media using neural networks},
author = {Alexander Christie and Matan Leibovich and Miguel Moscoso and Alexei Novikov and George Papanicolaou and Chrysoula Tsogka},
journal= {arXiv preprint arXiv:2410.21556},
year = {2024}
}