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.
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Ebrahimi, M., Martel, A.L. (2009). A PDE Approach to Coupled Super-Resolution with Non-parametric Motion. In: Cremers, D., Boykov, Y., Blake, A., Schmidt, F.R. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2009. Lecture Notes in Computer Science, vol 5681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03641-5_9
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DOI: https://doi.org/10.1007/978-3-642-03641-5_9
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