  Slow shutter speed and long exposure time of frame-based cameras often cause
visual blur and loss of inter-frame information, degenerating the overall
quality of captured videos. To this end, we present a unified framework of
event-based motion deblurring and frame interpolation for blurry video
enhancement, where the extremely low latency of events is leveraged to
alleviate motion blur and facilitate intermediate frame prediction.
Specifically, the mapping relation between blurry frames and sharp latent
images is first predicted by a learnable double integral network, and a fusion
network is then proposed to refine the coarse results via utilizing the
information from consecutive blurry inputs and the concurrent events. By
exploring the mutual constraints among blurry frames, latent images, and event
streams, we further propose a self-supervised learning framework to enable
network training with real-world blurry videos and events. Extensive
experiments demonstrate that our method compares favorably against the
state-of-the-art approaches and achieves remarkable performance on both
synthetic and real-world datasets.
