Stereo Video Deblurring

TL;DR

This paper introduces a novel stereo video deblurring method that utilizes 3D scene flow to accurately model motion blur, effectively handling multiple moving objects and reducing artifacts.

Contribution

It is the first to leverage stereo video and 3D scene flow for robust, accurate deblurring, surpassing previous methods in handling complex motions and artifacts.

Findings

Outperforms state-of-the-art deblurring methods on rendered and real videos.

Effectively handles multiple independently moving objects.

Reduces ringing artifacts through an iterative weighting scheme.

Abstract

Videos acquired in low-light conditions often exhibit motion blur, which depends on the motion of the objects relative to the camera. This is not only visually unpleasing, but can hamper further processing. With this paper we are the first to show how the availability of stereo video can aid the challenging video deblurring task. We leverage 3D scene flow, which can be estimated robustly even under adverse conditions. We go beyond simply determining the object motion in two ways: First, we show how a piecewise rigid 3D scene flow representation allows to induce accurate blur kernels via local homographies. Second, we exploit the estimated motion boundaries of the 3D scene flow to mitigate ringing artifacts using an iterative weighting scheme. Being aware of 3D object motion, our approach can deal robustly with an arbitrary number of independently moving objects. We demonstrate its benefit over state-of-the-art video deblurring using quantitative and qualitative experiments on rendered scenes and real videos.