Event-based cameras can measure intensity changes (called `{\it events}') with microsecond accuracy under high-speed motion and challenging lighting conditions. With the active pixel sensor (APS), the event camera allows simultaneous output of the intensity frames. However, the output images are captured at a relatively low frame-rate and often suffer from motion blur. A blurry image can be regarded as the integral of a sequence of latent images, while the events indicate the changes between the latent images. Therefore, we are able to model the blur-generation process by associating event data to a latent image. In this paper, we propose a simple and effective approach, the \textbf{Event-based Double Integral (EDI)} model, to reconstruct a high frame-rate, sharp video from a single blurry frame and its event data. The video generation is based on solving a simple non-convex optimization problem in a single scalar variable. Experimental results on both synthetic and real images demonstrate the superiority of our EDI model and optimization method in comparison to the state-of-the-art.
@article{arxiv.1811.10180,
title = {Bringing a Blurry Frame Alive at High Frame-Rate with an Event Camera},
author = {Liyuan Pan and Cedric Scheerlinck and Xin Yu and Richard Hartley and Miaomiao Liu and Yuchao Dai},
journal= {arXiv preprint arXiv:1811.10180},
year = {2018}
}