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.

本研究提出了一个新的基于事件的去模糊方法，通过利用不同空间和时间尺度的运动模糊信息，构建了一个适应实际场景的多尺度网络，通过自我监督学习来提高准确性，并引入一个包含多尺度模糊帧和事件的真实世界数据集以促进事件驱动去模糊研究。

泛化真实场景中的基于事件的运动去模糊

Generalizing Event-Based Motion Deblurring in Real-World Scenarios

In this paper, we propose an end-to-end learning framework for event-based
motion deblurring in a self-supervised manner, where real-world events are
exploited to alleviate the performance degradation caused by data
inconsistency. To achieve this end, optical flows are predicted from events,
with which the blurry consistency and photometric consistency are exploited to
enable self-supervision on the deblurring network with real-world data.
Furthermore, a piece-wise linear motion model is proposed to take into account
motion non-linearities and thus leads to an accurate model for the physical
formation of motion blurs in the real-world scenario. Extensive evaluation on
both synthetic and real motion blur datasets demonstrates that the proposed
algorithm bridges the gap between simulated and real-world motion blurs and
shows remarkable performance for event-based motion deblurring in real-world
scenarios.

本文提出了一种端到端学习框架，利用真实世界的事件减轻数据不一致性造成的性能下降，在光流的帮助下利用模糊一致性和亮度一致性实现了自监督，提出了分段线性运动模型以考虑运动的非线性，加强了对真实场景中运动模糊形成的准确建模，并在合成和真实运动模糊数据集上进行充分评估，显示了在真实世界情境中差距较大的模拟和真实运动模糊之间的良好表现。