Event-Based Motion Deblurring Using Task-Oriented 3D Gaussian Event Representations

Event-based motion deblurring has attracted increasing attention, as the high temporal resolution of event cameras provides motion cues unavailable to conventional RGB sensors, thereby enabling more effective deblurring. In realworld scenes, motion blur is often complex and nonlinear, with different regions exhibiting diverse motion speeds and directions. However, most existing approaches rely on handcrafted event representations that overlook such spatiotemporal motion heterogeneity, resulting in suboptimal deblurring performance. To address this limitation, we propose a learnable 3D Gaussian event representation module that adaptively selects key spatiotemporal coordinates for deblurring based on the blurred image content and the event density distribution. The event stream is then aggregated with 3D Gaussian weighting kernels to extract local motion features that are sensitive to motion direction and speed. Furthermore, to fully exploit the motion information encoded in the event representation, we adopt a twostage fusion strategy. In the first stage, local motion features are used to enhance fine detail restoration. In the second stage, a bidirectional attention fusion module leverages one-dimensional Gaussian-weighted event frames to correct global spatial misalignment, leading to more accurate structural alignment. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of our method and show that it consistently outperforms state-of-the-art approaches.