Event-driven Motion Deblurring via Trajectory-based Kernel Reconstruction

Recovering a sharp image from a motion-blurred observation remains challenging due to the inherently ill-posed nature of blind deblurring, especially under real-world non-uniform blur caused by complex camera and scene motion. Event cameras, with their microsecond-level temporal resolution and inherent sensitivity to motion, provide rich motion cues that can serve as strong priors for resolving such ambiguities. However, existing event-guided deblurring methods either rely on simplified blur assumptions or treat event data merely as generic features, without fully exploiting their physical relationship with the image formation process. In this paper, we propose a complete event-driven nonuniform blind deblurring framework that explicitly models the physical formation of spatially varying blur kernels. Specifically, we first estimate dense pixel-wise motion trajectories from the event stream via a differentiable event-alignment objective. These trajectories are then used to construct per-pixel point spread functions (PSFs), which provide a physically grounded blur operator. Based on this operator, we formulate a joint optimization framework that enforces blur consistency, event consistency, and image regularization. Finally, we design an unrolled optimization network that alternates between data-consistency reconstruction and learned image priors. Experiments on both synthetic and real-world datasets demonstrate that the proposed method improves deblurring performance while maintaining low computational complexity.