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

2025-11-28

AI Summary

Dynamic scattering media severely distort optical fields, degrading ghost imaging (GI) quality. To address this, we propose an embedded optical diffraction neural network (ODNN) for distortion correction. This ODNN features a fixed physical architecture—requiring no online feedback or retraining—and actively compensates for stochastic phase/amplitude distortions induced by dynamic scattering between the light source and object. It is tightly integrated with a single-pixel computational GI system and fully compatible with physics-informed reconstruction algorithms. The ODNN is pre-trained on simulated data and physically implemented in the optical path. Experimental validation using rotating single- or double-layer ground-glass diffusers demonstrates high-fidelity image reconstruction under severe undersampling (<10% measurements). The method exhibits strong robustness and significantly outperforms conventional approaches in imaging fidelity. This work establishes a new paradigm for real-time, efficient, non-invasive imaging in complex scattering environments.

Technology Category

• Search and Optimization: Distributed Search
• Computer Vision: Diffusion Models for Vision
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Security and Privacy: Data transparency and provenance

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.

Problem

• Overcomes scattering interference in ghost imaging
• Uses optical neural networks to correct distortions
• Enables high-quality imaging with undersampled data

Innovation

• ODNNs correct distortions in dynamic scattering media
• Fixed ODNNs trained on simulated datasets for correction
• Combines with physics-prior algorithms for undersampled imaging