The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

We analyze the super-resolution reconstruction constraints. In particular, we derive a sequence of results which all show that the constraints provide far less useful information as the magnification factor increases. It is well established that the use of a smoothness prior may help somewhat, however for large enough magnification factors any smoothness prior leads to overly smooth results. We therefore propose an algorithm that learns recognition-based priors for specific classes of scenes, the use of which gives far better super-resolution results for both faces and text.

Notes

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, "Limits on Super-Resolution and How to Break Them," Proceedings of the 2000 IEEE Conference on Computer Vision and Pattern Recognition, June, 2000.

BibTeX Reference

@inproceedings{Baker_2000_3276,
   author = "Simon Baker and Takeo Kanade",
   title = "Limits on Super-Resolution and How to Break Them",
   booktitle = "Proceedings of the 2000 IEEE Conference on Computer Vision and Pattern Recognition",
   month = "June",
   year = "2000",
}

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