Degradation-Adaptive Denoising: Aligning Diffusion Models With Physics of Video Snapshot Compressive Imaging.

Abstract

Video Snapshot Compressive Imaging (SCI) captures multiple video frames in a single exposure, enabling efficient reconstruction of high-speed scenes for motion analysis and event detection. Existing SCI in coded aperture compressive temporal imaging (CACTI) methods predominantly rely on feedforward deep networks with fixed denoising strategies. However, they lack alignment with the SCI physical inverse model and struggle to balance motion detail recovery and static background smoothing. In this paper, we propose PCD-Diffusion for Video SCI, the first diffusion-based reconstruction framework for Video SCI, which reformulates the inverse problem as a progressive denoising process. Specifically, we design a Physically-Constrained Dynamic Diffusion (PCD-Diffusion) model, introducing a region-adaptive diffusion schedule and spatiotemporal residual estimation. This method explicitly aligns the denoising process with SCI's spatially non-uniform and temporally evolving residual distribution. Additionally, a motion prior-guided diffusion schedule and a Gauss-guided spatiotemporal adaptive residual estimation dynamically steer the denoising trajectory, ensuring accurate motion detail restoration and physically consistent reconstructions. Extensive results on simulated and real datasets verify the superior reconstruction fidelity and temporal coherence of the proposed PCD-Diffusion framework over existing approaches. Code will be released upon publication.