  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.

Computer Vision and Pattern Recognition