Optical diffraction neural networks assisted computational ghost imaging through dynamic scattering media
This addresses imaging problems in fields like biomedical or remote sensing where dynamic scattering media interfere with ghost imaging, though it appears incremental as it builds on existing ghost imaging and neural network correction techniques.
The paper tackles the challenge of ghost imaging through dynamic scattering media, which is sensitive to scattering between the light source and object, by proposing an optical diffraction neural networks (ODNNs) assisted method that actively corrects distortions; experimental validation with rotating ground glass confirms feasibility, and combining it with physics-prior-based algorithms enables high-quality imaging under undersampled conditions.
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.