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.

翻译：我们提出DeblurSR，一种将模糊图像转化为清晰视频的新型运动去模糊方法。该方法利用事件数据补偿运动模糊，并采用脉冲表示将清晰输出视频参数化为时间到强度的映射。我们的核心贡献——脉冲表示（Spiking Representation, SR），灵感源于决定生物神经元在生命体中通信方式的神经形态原理。我们讨论了脉冲为何能表征清晰边缘，以及如何从神经形态视角解释脉冲参数。相比最先进的基于事件驱动的运动去模糊方法，DeblurSR具有更高的输出质量且计算资源消耗更少。我们进一步表明，该方法结合隐式神经表示的最新进展后，可轻松扩展至视频超分辨率任务。DeblurSR的实现与动画可视化见https://github.com/chensong1995/DeblurSR。