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 (Formula presented.) 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 (Formula presented.) OME range where other methods fail, it still reliably reconstructs complex scenes with a PSNR of 27.21 dB.