  Event cameras are bio-inspired sensors that asynchronously report intensity
changes in microsecond resolution. DAVIS can capture high dynamics of a scene
and simultaneously output high temporal resolution events and low frame-rate
intensity images. In this paper, we propose a single image (potentially
blurred) and events based optical flow estimation approach. First, we
demonstrate how events can be used to improve flow estimates. To this end, we
encode the relation between flow and events effectively by presenting an
event-based photometric consistency formulation. Then, we consider the special
case of image blur caused by high dynamics in the visual environments and show
that including the blur formation in our model further constrains flow
estimation. This is in sharp contrast to existing works that ignore the blurred
images while our formulation can naturally handle either blurred or sharp
images to achieve accurate flow estimation. Finally, we reduce flow estimation,
as well as image deblurring, to an alternative optimization problem of an
objective function using the primal-dual algorithm. Experimental results on
both synthetic and real data (with blurred and non-blurred images) show the
superiority of our model in comparison to state-of-the-art approaches.
