Simultaneous Stereo Video Deblurring and Scene Flow Estimation
This addresses the issue of blur in outdoor stereo videos for applications like robotics or autonomous driving, representing a novel integration rather than an incremental step.
The paper tackles the problem of blur in stereo videos caused by motion and depth variations by proposing a joint framework for simultaneous deblurring and scene flow estimation, achieving significant improvements over state-of-the-art methods in both flow estimation and blur removal.
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.