  Traditional frame-based cameras inevitably suffer from motion blur due to
long exposure times. As a kind of bio-inspired camera, the event camera records
the intensity changes in an asynchronous way with high temporal resolution,
providing valid image degradation information within the exposure time. In this
paper, we rethink the eventbased image deblurring problem and unfold it into an
end-to-end two-stage image restoration network. To effectively fuse event and
image features, we design an event-image cross-modal attention module applied
at multiple levels of our network, which allows to focus on relevant features
from the event branch and filter out noise. We also introduce a novel symmetric
cumulative event representation specifically for image deblurring as well as an
event mask gated connection between the two stages of our network which helps
avoid information loss. At the dataset level, to foster event-based motion
deblurring and to facilitate evaluation on challenging real-world images, we
introduce the Real Event Blur (REBlur) dataset, captured with an event camera
in an illumination controlled optical laboratory. Our Event Fusion Network
(EFNet) sets the new state of the art in motion deblurring, surpassing both the
prior best-performing image-based method and all event-based methods with
public implementations on the GoPro dataset (by up to 2.47dB) and on our REBlur
dataset, even in extreme blurry conditions. The code and our REBlur dataset
will be made publicly available.
