Super-resolution in disordered media using neural networks
This work addresses imaging challenges in disordered media, potentially benefiting fields like medical imaging or materials science, but appears incremental as it builds on existing super-resolution concepts.
The researchers tackled the problem of imaging in strongly scattering media by estimating the ambient medium's Green's functions using large datasets, achieving super-resolution with better resolution than in a homogeneous medium.
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.