DeblurSR: Event-Based Motion Deblurring Under the Spiking Representation
This work addresses motion deblurring for computer vision applications, offering a novel neuromorphic-inspired approach with practical efficiency gains.
The paper tackles motion deblurring by converting blurry images into sharp videos using event data and a spiking representation, achieving higher output quality and lower computational requirements than state-of-the-art methods.
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.