@conference {Sellent2016,
	title = {Stereo video deblurring},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {9906 LNCS},
	year = {2016},
	pages = {558{\textendash}575},
	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, Scene flow, Spatially-variant deblurring},
	isbn = {9783319464749},
	issn = {16113349},
	doi = {10.1007/978-3-319-46475-6_35},
	author = {Sellent, Anita and Carsten Rother and Roth, Stefan}
}
@techreport {Sellent,
	title = {Stereo Video Deblurring-Supplemental Material},
	year = {2016},
	abstract = {One of the key contributions in our stereo video deblurring is to employ 3D scene flow to induce blur kernels based on homographies. As the difference to inducing blur kernels from an optical flow field may seem subtle but increases deblurring performance considerably, we schematically illustrate the difference of the two ways of generating blur kernels in Fig. 10. (a) 3D motion (b) Image plane projection: Blur kernels from linear displacements (c) Image plane projection: Blur kernels from homographies Fig. 10. Inducing blur matrices: (a) Assume that 3D point P moves with a constant rigid body motion in 3D, e. g. with a yaw motion. The projection of this motion to the image plane (blue) is a circular trajectory. The corresponding 2D ground truth displacement (yellow), however, is a vector in the image plane that connects start and end point of the motion during a time interval. (b) Using optical flow, the blur kernel at a location z is approximated by identifying all pixels that, according to their spatially-variant displacement, pass through z during the time interval. The image content at point x is correctly identified as passing through z. The image content at point y is not identified correctly as its 2D displacement passes z at a distance. Instead, the image content at point{\textasciicircum}ypoint{\textasciicircum} point{\textasciicircum}y is erroneously identified as passing through z, even though{\textasciicircum}ythough{\textasciicircum} though{\textasciicircum}y has a different distance to the rotation center than z. This results in the distorted kernels shown in Fig. 3b. (c) Assuming 3D points in the vicinity of P to form a plane, we employ 3D homographies to generate blur kernels. The blur kernel at location z is thus formed by the image points whose trajectory, according to the homography, passes through z during the time interval. Consequently, y is correctly identified as passing through z, leading to the kernels shaped as circular arcs shown in Fig. 3a},
	author = {Sellent, Anita and Carsten Rother and Roth, Stefan}
}
