  Recovering sharp video sequence from a motion-blurred image is highly
ill-posed due to the significant loss of motion information in the blurring
process. For event-based cameras, however, fast motion can be captured as
events at high time rate, raising new opportunities to exploring effective
solutions. In this paper, we start from a sequential formulation of event-based
motion deblurring, then show how its optimization can be unfolded with a novel
end-to-end deep architecture. The proposed architecture is a convolutional
recurrent neural network that integrates visual and temporal knowledge of both
global and local scales in principled manner. To further improve the
reconstruction, we propose a differentiable directional event filtering module
to effectively extract rich boundary prior from the stream of events. We
conduct extensive experiments on the synthetic GoPro dataset and a large newly
introduced dataset captured by a DAVIS240C camera. The proposed approach
achieves state-of-the-art reconstruction quality, and generalizes better to
handling real-world motion blur.
