Video Super Resolution Using Duality Based TV-L1 Optical Flow

Authors:
Dennis Mitzel;Thomas Pock;Thomas Schoenemann;Daniel Cremers
Affiliations:
Department of Computer Science, University of Bonn, Germany and UMIC Research Centre, RWTH Aachen, Germany;Institute for Computer Graphics and Vision, TU Graz, Austria;Department of Computer Science, University of Bonn, Germany;Department of Computer Science, University of Bonn, Germany
Venue:
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Year:
2009

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Abstract

In this paper, we propose a variational framework for computing a superresolved image of a scene from an arbitrary input video. To this end, we employ a recently proposed quadratic relaxation scheme for high accuracy optic flow estimation. Subsequently we estimate a high resolution image using a variational approach that models the image formation process and imposes a total variation regularity of the estimated intensity map. Minimization of this variational approach by gradient descent gives rise to a deblurring process with a nonlinear diffusion. In contrast to many alternative approaches, the proposed algorithm does not make assumptions regarding the motion of objects. We demonstrate good experimental performance on a variety of real-world examples. In particular we show that the computed super resolution images are indeed sharper than the individual input images.