Exploit imaging through opaque wall via deep learning

TL;DR

This paper presents a deep learning approach to reconstruct images of objects hidden behind opaque scattering media, successfully retrieving images through a 3mm thick scattering medium using neural networks.

Contribution

It introduces a novel deep neural network method for imaging through thick scattering media, advancing the capability to see behind opaque obstacles.

Findings

Successfully retrieved images behind a 3mm thick scattering medium

Deep learning significantly improves imaging through opaque media

Method applicable to thick scattering media with high optical depth

Abstract

Imaging through scattering media is encountered in many disciplines or sciences, ranging from biology, mesescopic physics and astronomy. But it is still a big challenge because light suffers from multiple scattering is such media and can be totally decorrelated. Here, we propose a deep-learning-based method that can retrieve the image of a target behind a thick scattering medium. The method uses a trained deep neural network to fit the way of mapping of objects at one side of a thick scattering medium to the corresponding speckle patterns observed at the other side. For demonstration, we retrieve the images of a set of objects hidden behind a 3mm thick white polystyrene slab, the optical depth of which is 13.4 times of the scattering mean free path. Our work opens up a new way to tackle the longstanding challenge by using the technique of deep learning.