用神经网络提升散射介质中的成像分辨率，突破均匀介质极限。
Super-resolution in disordered media using neural networks
- 利用大规模数据训练神经网络估计强散射介质的格林函数。
- 实现分辨率优于均匀介质的成像效果，达超分辨率水平。
- 适合光学成像、生物医学成像领域研究者参考。
我们提出一种方法，利用大规模多样数据集精确估计强散射介质中环境介质的格林函数。在使用和不使用神经网络的情况下获得这些估计后，均实现了优异的成像结果，其分辨率优于均匀介质。这一现象即所谓的超分辨率，源于环境散射介质有效扩展了物理成像孔径。该工作已提交至IEEE，可能发表。版权或会转移，之后此版本可能不再可访问。
原文摘要 · Abstract (English)
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.
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