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RI | Publications | Super-Resolution: Reconstruction or Recognition?
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Text only version of this site
Super-Resolution: Reconstruction or Recognition?
S. Baker and T. Kanade
IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, IEEE, Baltimore, Maryland, June, 2001.
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| Abstract |
Super-resolution is usually posed as a reconstruction problem. The low resolution input images are assumed to be noisy, down-sampled versions of an unknown super-resolution image that is to be estimated. A common way of inverting the down-sampling process is to write down the reconstruction constraints and then solve them, often adding a smoothness prior to regularize the solution. In this paper, we present two results which both show that there is more to super-resolution than image reconstruction. We first analyze the reconstruction constraints and show that they provide less and less useful information as the magnification factor increases. Afterwards, we describe a ``hallucination'' algorithm, incorporating the recognition of local features in the low resolution images, which outperforms existing reconstruction-based algorithms.
| Notes |
Sponsor: US DOD
Grant ID: MDA-904-98-C-A915
Associated center: VASC
Associated lab/group: Face Group
Associated projects: Photometric Limits on Computer Vision, Image Enhancement for Faces, and Hallucinating Faces
| Text Reference |
S. Baker and T. Kanade, "Super-Resolution: Reconstruction or Recognition?," IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, IEEE, Baltimore, Maryland, June, 2001.
| BibTeX Reference |
@inproceedings{Baker_2001_3504,
author = "Simon Baker and Takeo Kanade",
title = "Super-Resolution: Reconstruction or Recognition?",
booktitle = "IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing",
month = "June",
year = "2001",
publisher = "IEEE",
address = "Baltimore, Maryland"
}