Real-time imaging through dynamic scattering media enabled by fixed optical modulations

Abstract

Dynamic scattering remains a significant challenge to the practical deployment of anti-scattering imaging. Existing methods, such as transmission matrix measurements, iterative wavefront shaping, and optical phase conjugation, depend on a quasi-static assumption, requiring the object and scattering medium to remain stable during a single imaging process to enable one-to-one compensation. However, image reconstruction becomes unattainable when this assumption is violated. Here, we propose a novel imaging strategy that counteracts time-dependent scattering perturbations through a fixed modulation module. This one-to-many compensation mechanism is realized via optical diffraction neural networks (ODNNs) trained on simulated datasets. For the first time, we reveal that its feasibility stems from the optical shower-curtain effect, and its effectiveness typically within 1-2 transport mean free paths. Our approach is not only immune to speckle decorrelation but also leverages it to enhance image quality. ODNNs generalize effectively to real-world scattering medium scenarios via multi-simulated scattering media learning and reconstruct images in real time with light-speed processing. We achieve 80 Hz imaging of moving objects in dynamic scattering media with decorrelation times < 1 ms, demonstrating applicability in large-field-of-view imaging and incoherent illumination scenarios. This work lays the foundation for high-speed, intelligent optical imaging, advancing practical anti-dynamic scattering techniques.