用光学神经网络矫正动态散射，实现穿透干扰的高质量鬼成像
Optical diffraction neural networks assisted computational ghost imaging through dynamic scattering media
- 引入预训练的光学衍射神经网络实时校正散射畸变
- 在旋转玻璃板实验中实现清晰成像，重建信噪比提升显著
- 适合需在低采样率下成像的科研与工业场景
鬼成像利用无空间分辨能力的单像素探测器捕捉物体回波强度信号，通过与照明图案的相关性重构图像。该架构天然抑制了物体与探测器间的散射干扰，但对光源与物体之间的散射敏感。为解决此问题，我们提出一种基于光学衍射神经网络（ODNNs）的鬼成像方法，用于穿透动态散射介质。在方案中，一组在模拟数据上训练好的固定ODNN被嵌入实验光路，主动修正由动态散射介质引起的随机畸变。通过旋转单层和双层磨砂玻璃的实验验证了方法的可行性与有效性。此外，本方案还可与基于物理先验的重建算法结合，在欠采样条件下实现高质量成像。该工作展示了一种新型穿透动态散射介质的成像策略，可推广至其他成像系统。
原文摘要 · Abstract (English)
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.
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