Optical Diffraction Neural Network Assisted Computational Ghost Imaging Through Dynamic Scattering Media

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 is sensitive to scattering between the light source and the object. To address this challenge, we propose an optical diffraction neural network (ODNN) 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 under-sampled conditions. This work demonstrates, to our knowledge, a novel strategy for imaging through dynamic scattering media, which can be extended to other imaging systems.