A convex approach for variational super-resolution

Authors:
Markus Unger;Thomas Pock;Manuel Werlberger;Horst Bischof
Affiliations:
Institute for Computer Graphics and Vision, Graz University of Technology, Austria;Institute for Computer Graphics and Vision, Graz University of Technology, Austria;Institute for Computer Graphics and Vision, Graz University of Technology, Austria;Institute for Computer Graphics and Vision, Graz University of Technology, Austria
Venue:
Proceedings of the 32nd DAGM conference on Pattern recognition
Year:
2010

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Abstract

We propose a convex variational framework to compute high resolution images from a low resolution video. The image formation process is analyzed to provide to a well designed model for warping, blurring, downsampling and regularization. We provide a comprehensive investigation of the single model components. The super-resolution problem is modeled as a minimization problem in an unified convex framework, which is solved by a fast primal dual algorithm. A comprehensive evaluation on the influence of different kinds of noise is carried out. The proposed algorithm shows excellent recovery of information for various real and synthetic datasets.