Time-Specialized Event-Image Alignment for Blur-to-Video Decomposition

Zhijing Sun, Senyan Xu, Ruixuan Jiang, Kean Liu, Runze Tian, Xueyang Fu, Zheng-Jun Zha; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026, pp. 8045-8055

Motion blur is a common degradation in dynamic imaging. Recent studies have moved beyond restoring a single sharp image from a blurred input and instead target blur decomposition: recovering a temporally continuous sharp video sequence from one motion-blurred image. Event cameras, with their microsecond temporal resolution, can effectively alleviate motion ambiguity. However, existing event-based methods often fail to explicitly model time-aligned event-image features. How to accurately exploit event data to reconstruct frames at different time instants remains largely underexplored. In this paper, we propose TSANet, an event-based blur-to-video decomposition method that time-specializes both event features and image features for alignment. Specifically, we introduce a Relative Time-Encoded Attention module that steers event features toward motion information relevant to a given target time, and a Timesurface Dynamic Warping module that warps image features into the spatial configuration corresponding to that time. With time-specialized motion and image features explicitly aligned at arbitrary query times, our framework can decompose a single blurred image into a high-frame-rate sharp video sequence. In addition, we collect a new dataset containing real events and high-quality color videos, and synthesize blurred inputs by averaging sharp frames to evaluate our method. Experiments on multiple datasets with both synthetic and real events demonstrate that our approach consistently outperforms previous state-of-the-art methods on the blur decomposition task.