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.

翻译：基于事件的运动去模糊通过利用低延迟事件已展现出令人鼓舞的结果。然而，当前方法在实际应用中存在局限性，因为它们假设输入具有相同的空间分辨率以及特定的模糊分布。本文针对这些局限性，旨在通用化现实场景中基于事件的去模糊性能。我们提出了一种尺度感知网络，该网络支持灵活输入空间尺度，并能从不同时间尺度的运动模糊中学习。随后开发了一种两阶段自监督学习方案，以拟合真实数据分布。通过利用模糊性的相对性，我们的方法有效保证了潜在图像的亮度与结构复原，并通过自蒸馏方式进一步通用化去模糊性能，以处理不同空间和时间尺度的运动模糊。本文对方法进行了广泛评估，展示了卓越性能，并引入了一个包含多尺度模糊帧及事件的真实世界数据集，以促进基于事件去模糊的研究。