A PDE Approach to Coupled Super-Resolution with Non-parametric Motion

Authors:
Mehran Ebrahimi;Anne L. Martel
Affiliations:
Department of Medical Biophysics, University of Toronto Imaging Research, Sunnybrook Health Sciences Centre, Toronto, Canada;Department of Medical Biophysics, University of Toronto Imaging Research, Sunnybrook Health Sciences Centre, Toronto, Canada
Venue:
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Year:
2009

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Abstract

The problem of recovering a high-resolution image from a set of distorted (e.g., warped, blurred, noisy) and low-resolution images is known as super-resolution . Accurate motion estimation among the low-resolution measurements is a fundamental challenge of the super-resolution problem. Some recent promising advances in this area have been focused on coupling or combing the super-resolution reconstruction and the motion estimation. However, the existing approach is limited to parametric motion models, e.g., affine. In this paper, we shall address the coupled super-resolution problem with a non-parametric motion model. We address the problem in a variational formulation and propose a PDE-approach to yield a numerical scheme. In this approach, we use diffusion regularizations for both the motion and the super-resolved image. However, the approach is flexible and other suitable regularization schemes may be employed in the proposed formulation.