Ultra-Wide-Field Noninvasive Imaging Through Scattering Media Via Physics-Guided Deep Learning

Noninvasive imaging through scattering media is crucial for diverse applications but remains constrained by a narrow field of view (FOV). Although recent learning-based methods have a larger FOV, they often require large-scale real experimental datasets and struggle when the FOV is far beyond the optical memory effect (OME). Here, we propose a physics-guided adaptive dual-domain diffusion model for ultra-wide-field noninvasive imaging through scattering media, namely UNI-Net. Specifically, we first develop a physical scattering imaging model to synthesize large-scale pre-training data, thereby reducing dependence on real experimental datasets. Second, to maximize the utilization of speckle information, we partition each speckle pattern into multi-channel patches to guide the diffusion process. Third, we propose a spatial-channel parallel attention block to model the spatial sparsity and inter-channel similarity of speckle patches with linear complexity. Extensive experiments show that our method cuts reliance on real experimental data by an order of magnitude and achieves a PSNR of 31.23 dB at a 41 $\times$ OME range in complex scenes, which is 49.5% higher than existing approaches while requiring significantly lower computational and memory costs. Even at an extreme 164 $\times$ OME range where other methods fail, it still reliably reconstructs complex scenes with a PSNR of 27.21 dB.