  Event-based motion deblurring has shown promising results by exploiting
low-latency events. However, current approaches are limited in their practical
usage, as they assume the same spatial resolution of inputs and specific
blurriness distributions. This work addresses these limitations and aims to
generalize the performance of event-based deblurring in real-world scenarios.
We propose a scale-aware network that allows flexible input spatial scales and
enables learning from different temporal scales of motion blur. A two-stage
self-supervised learning scheme is then developed to fit real-world data
distribution. By utilizing the relativity of blurriness, our approach
efficiently ensures the restored brightness and structure of latent images and
further generalizes deblurring performance to handle varying spatial and
temporal scales of motion blur in a self-distillation manner. Our method is
extensively evaluated, demonstrating remarkable performance, and we also
introduce a real-world dataset consisting of multi-scale blurry frames and
events to facilitate research in event-based deblurring.
