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We present DeblurSR, a novel motion deblurring approach that converts ablurry image into a sharp video. DeblurSR utilizes event data to compensate formotion ambiguities and exploits the spiking representation to parameterize thesharp output video as a mapping from time to intensity. Our key contribution,the Spiking Representation (SR), is inspired by the neuromorphic principlesdetermining how biological neurons communicate with each other in livingorganisms. We discuss why the spikes can represent sharp edges and how thespiking parameters are interpreted from the neuromorphic perspective. DeblurSRhas higher output quality and requires fewer computing resources thanstate-of-the-art event-based motion deblurring methods. We additionally showthat our approach easily extends to video super-resolution when combined withrecent advances in implicit neural representation. The implementation andanimated visualization of DeblurSR are available athttps://github.com/chensong1995/DeblurSR.

DeblurSR: Event-Based Motion Deblurring Under the Spiking Representation