BVSR-EvD: Blurry Video Space-Time Super-Resolution With Events via Diffusion Models

Video restoration from low-resolution and low-frame-rate blurry sources remains challenging due to insufficient data priors. In this paper, we propose BVSR-EvD, leveraging event cameras and diffusion models to boost blurry video space-time super-resolution. Specifically, we identify three distinct data priors from event-video dual modalities: motion prior from events, content prior from videos, and physical prior from their integration, contributing to temporal stability, content preservation, and detail enhancement respectively. To effectively utilize these data priors, BVSR-EvD creates the Trident Diffusion Model (Trident-DM), which decomposes each denoising step into trident decoupling and adaptive self-composition stages. The former employs single-modal and dual-modal meta-networks to extract the three unique data priors, while the latter dynamically integrates them through learned prior-aware weight maps. BVSR-EvD achieves up to $\times 8$ spatial super-resolution and $\times 64$ temporal super-resolution from blurry videos, surpassing existing methods on public video datasets.