  Non-uniform image deblurring is a challenging task due to the lack of
temporal and textural information in the blurry image itself. Complementary
information from auxiliary sensors such event sensors are being explored to
address these limitations. The latter can record changes in a logarithmic
intensity asynchronously, called events, with high temporal resolution and high
dynamic range. Current event-based deblurring methods combine the blurry image
with events to jointly estimate per-pixel motion and the deblur operator. In
this paper, we argue that a divide-and-conquer approach is more suitable for
this task. To this end, we propose to use modulated deformable convolutions,
whose kernel offsets and modulation masks are dynamically estimated from events
to encode the motion in the scene, while the deblur operator is learned from
the combination of blurry image and corresponding events. Furthermore, we
employ a coarse-to-fine multi-scale reconstruction approach to cope with the
inherent sparsity of events in low contrast regions. Importantly, we introduce
the first dataset containing pairs of real RGB blur images and related events
during the exposure time. Our results show better overall robustness when using
events, with improvements in PSNR by up to 1.57dB on synthetic data and 1.08 dB
on real event data.
