Optical diffraction neural networks assisted computational ghost imaging through dynamic scattering media

TL;DR

This paper introduces an optical diffraction neural network-assisted ghost imaging technique that actively corrects distortions caused by dynamic scattering media, enabling clearer imaging through challenging environments.

Contribution

The authors develop a novel ODNN-assisted ghost imaging method trained on simulated data to correct scattering distortions in real-time imaging scenarios.

Findings

Validated with rotating ground glass experiments

Effective in undersampled imaging conditions

Compatible with physics-prior-based reconstruction algorithms

Abstract

Ghost imaging leverages a single-pixel detector with no spatial resolution to acquire object echo intensity signals, which are correlated with illumination patterns to reconstruct an image. This architecture inherently mitigates scattering interference between the object and the detector but sensitive to scattering between the light source and the object. To address this challenge, we propose an optical diffraction neural networks (ODNNs) assisted ghost imaging method for imaging through dynamic scattering media. In our scheme, a set of fixed ODNNs, trained on simulated datasets, is incorporated into the experimental optical path to actively correct random distortions induced by dynamic scattering media. Experimental validation using rotating single-layer and double-layer ground glass confirms the feasibility and effectiveness of our approach. Furthermore, our scheme can also be combined with physics-prior-based reconstruction algorithms, enabling high-quality imaging under undersampled conditions. This work demonstrates a novel strategy for imaging through dynamic scattering media, which can be extended to other imaging systems.