Stereo Video Deblurring
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
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Technische Universit¨ at Dresden, Dresden, Germany [email protected] Technische Universit¨ at Darmstadt, Darmstadt, Germany
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-ofthe-art video deblurring using quantitative and qualitative experiments on rendered scenes and real videos.
Keywords: Object motion blur deblurring
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Scene flow
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Spatially-variant
Introduction
Stereo is one of the oldest areas of computer vision research [1]. Interestingly, the arrival of mass-produced active depth sensors [2] seems to have renewed interest also in passive stereo systems. In contrast to active depth sensors, stereo cameras are also applicable in outdoor environments. Due to their more general applicability, stereo cameras are gaining increased adoption, for example in autonomous driving [3]. Remarkably, the availability of stereo image pairs also helps in the estimation of temporal correspondences: On the KITTI optical flow benchmark [4], the best performing algorithms [5,6] are indeed scene flow algorithms that jointly estimate depth and 3D motion from stereo videos. Part of their advantage stems from an increased robustness to adverse imaging conditions [6]. One such adverse imaging condition is a shortage of light. In low-light conditions, Electronic supplementary material The online version of this chapter (doi:10. 1007/978-3-319-46475-6 35) contains supplementary material, which is available to authorized users. c Springer International Publishing AG 2016 B. Leibe et al. (Eds.): ECCV 2016, Part II, LNCS 9906, pp. 558–575, 2016. DOI: 10.1007/978-3-319-46475-6 35
Stereo Video Deblurring
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the exposure time often needs to be increased to obtain a reasonable signal-tonoise-ratio. But when either the camera, or the objects in the scenes are moving during exposure time, this results in motion blurred images. Motion blur is not only unsatisfactory to look at, it can also disturb further image-based processing, e.g. in tasks such as panorama stitching [8] or barcode recognition [9]. In stereo video setups, viewpoint-dependent motion blur hinders a post-capture adjustment of the baseline, the acquisition and visualization of 3D point clouds (see Fig
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