Abstract
Videos for outdoor scene often show unpleasant blur effects due to the large relative motion between the camera
and the dynamic objects and large depth variations. Existing works typically focus monocular video deblurring. In
this paper, we propose a novel approach to deblurring from
stereo videos. In particular, we exploit the piece-wise planar assumption about the scene and leverage the scene flow
information to deblur the image. Unlike the existing approach [31] which used a pre-computed scene flow, we propose a single framework to jointly estimate the scene flow
and deblur the image, where the motion cues from scene
flow estimation and blur information could reinforce each
other, and produce superior results than the conventional
scene flow estimation or stereo deblurring methods. We
evaluate our method extensively on two available datasets
and achieve significant improvement in flow estimation and
removing the blur effect over the state-of-the-art methods.