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Carnegie Mellon Robotics Institute

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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(s) / Consortia: Vision and Autonomous Systems Center

Associated Lab(s) / Group(s): Face Group

Associated Project(s): Photometric Limits on Computer Vision, Image Enhancement for Faces, Hallucinating Faces

Text Reference

Simon Baker and Takeo Kanade, "Super-Resolution: Reconstruction or Recognition?," IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, 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",
   publisher = "IEEE",
   address = "Baltimore, Maryland",
   month = "June",
   year = "2001",
}

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