  The idea of video super resolution is to use different view points of a
single scene to enhance the overall resolution and quality. Classical energy
minimization approaches first establish a correspondence of the current frame
to all its neighbors in some radius and then use this temporal information for
enhancement. In this paper, we propose the first variational super resolution
approach that computes several super resolved frames in one batch optimization
procedure by incorporating motion information between the high-resolution image
frames themselves. As a consequence, the number of motion estimation problems
grows linearly in the number of frames, opposed to a quadratic growth of
classical methods and temporal consistency is enforced naturally. We use
infimal convolution regularization as well as an automatic parameter balancing
scheme to automatically determine the reliability of the motion information and
reweight the regularization locally. We demonstrate that our approach yields
state-of-the-art results and even is competitive with machine learning
approaches.
