# Deep Hybrid Scattering Image Learning

arXiv: https://arxiv.org/abs/1809.07706 · HTML: https://mlsanity.com/paper/1809.07706/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2018.

## Built on by (most cited first, 1 of 1)

- [Analysis of Diffractive Neural Networks for Seeing Through Random Diffusers](https://mlsanity.com/paper/2205.00428/) (2022)

## Abstract (arXiv)

A well-trained deep neural network is shown to gain capability of simultaneously restoring two kinds of images, which are completely destroyed by two distinct scattering medias respectively. The network, based on the U-net architecture, can be trained by blended dataset of speckles-reference images pairs. We experimentally demonstrate the power of the network in reconstructing images which are strongly diffused by glass diffuser or multi-mode fiber. The learning model further shows good generalization ability to reconstruct images that are distinguished from the training dataset. Our work facilitates the study of optical transmission and expands machine learning's application in optics.