  We present DeblurSR, a novel motion deblurring approach that converts a
blurry image into a sharp video. DeblurSR utilizes event data to compensate for
motion ambiguities and exploits the spiking representation to parameterize the
sharp output video as a mapping from time to intensity. Our key contribution,
the Spiking Representation (SR), is inspired by the neuromorphic principles
determining how biological neurons communicate with each other in living
organisms. We discuss why the spikes can represent sharp edges and how the
spiking parameters are interpreted from the neuromorphic perspective. DeblurSR
has higher output quality and requires fewer computing resources than
state-of-the-art event-based motion deblurring methods. We additionally show
that our approach easily extends to video super-resolution when combined with
recent advances in implicit neural representation. The implementation and
animated visualization of DeblurSR are available at
https://github.com/chensong1995/DeblurSR.
