Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-based single document image super-resolution: a global MAP approach with outlier rejection
Multidimensional Systems and Signal Processing
A non-local regularization strategy for image deconvolution
Pattern Recognition Letters
Non-local Regularization of Inverse Problems
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Manifold models for signals and images
Computer Vision and Image Understanding
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Image super-resolution techniques provide a route to studying fine scale anatomical detail using one or more lower resolution acquisitions. A crucial issue in such algorithms is the form of image regularization used to constrain the image structure at points where there are insufficient data values. In this paper we examine the specific problem of reconstructing a high resolution isotropic image when presented with a set of low-resolution anisotropic images. In particular here, we propose to extend recently proposed patch-based methods for super resolution to this problem. More specifically, we develop regularization term which is designed to take advantage of information redundancy in the set of images. We include an experimental evaluation using the MR Brainweb database and a comparison which shows significantly improved reconstruction details when compared to conventional interpolation based methods.