  Event-based cameras measure intensity changes (called `events') with
microsecond accuracy under high-speed motion and challenging lighting
conditions. With the `active pixel sensor' (APS), the `Dynamic and Active-pixel
Vision Sensor' (DAVIS) allows the simultaneous output of intensity frames and
events. However, the output images are captured at a relatively low frame rate
and often suffer from motion blur. A blurred image can be regarded as the
integral of a sequence of latent images, while events indicate changes between
the latent images. Thus, we are able to model the blur-generation process by
associating event data to a latent sharp image. Based on the abundant event
data alongside a low frame rate, easily blurred images, we propose a simple yet
effective approach to reconstruct high-quality and high frame rate sharp
videos. Starting with a single blurred frame and its event data from DAVIS, we
propose the Event-based Double Integral (EDI) model and solve it by adding
regularization terms. Then, we extend it to multiple Event-based Double
Integral (mEDI) model to get more smooth results based on multiple images and
their events. Furthermore, we provide a new and more efficient solver to
minimize the proposed energy model. By optimizing the energy function, we
achieve significant improvements in removing blur and the reconstruction of a
high temporal resolution video. 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 datasets demonstrate the
superiority of our mEDI model and optimization method compared to the
state-of-the-art.
