EDVD: Cross-Modal Spatio-Temporal Fusion With Event and Diffusion for Video Deblurring

Restoring high-quality images from blurred videos is a highly challenging task, especially in severely blurred scenes. In recent years, event-based methods have achieved significant progress in video deblurring. However, the modal differences between the event and image increase the difficulty of feature fusion. Additionally, the sparsity of event makes it difficult to restore some local details. To address these issues, we propose a new video deblurring method. Firstly, we design a cross-modal collaborative attention mechanism to effectively fuse features from blurred frames and event frames, thereby deeply extracting motion information from event frames. Secondly, we utilize a diffusion model to generate spatial guiding prior feature, enhancing local details and textures. Furthermore, we propose an event-guided dynamic feature fusion module that adaptively integrates spatio-temporal information from neighboring frames. Experimental results on both synthetic and real datasets demonstrate that our method outperforms the current state-of-the-art approaches. The code is available at: https://github.com/Frank-Zhou-01/EDVD-main.