Limits on Super-Resolution and How to Break Them

Simon Baker and Takeo Kanade
Proceedings of the 2000 IEEE Conference on Computer Vision and Pattern Recognition, July, 2000.


Download
  • Adobe portable document format (pdf) (328KB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

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, July, 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 = "July",
   year = "2000",
}