Summary of Super-resolution in Disordered Media Using Neural Networks, by Alexander Christie et al.
Super-resolution in disordered media using neural networks
by Alexander Christie, Matan Leibovich, Miguel Moscoso, Alexei Novikov, George Papanicolaou, Chrysoula Tsogka
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed methodology leverages large and diverse datasets to accurately estimate Green’s functions in strongly scattering media. Neural networks are employed to achieve excellent imaging results, surpassing those of a homogeneous medium through a phenomenon known as super-resolution. This occurs due to the ambient scattering medium enhancing the physical imaging aperture. The presented work has been submitted for possible publication in the IEEE. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine using special data sets to create really clear images of things that are hard to see clearly, like objects under water or inside thick walls. That’s what this paper is about! Scientists developed a new way to use these big datasets and special computer programs called neural networks to take blurry pictures and make them super sharp. This means we can “see” things more clearly than before, which could be really helpful in fields like medicine or engineering. |
Keywords
» Artificial intelligence » Super resolution
