Referenced Items are not available for this document.
We present a learning-based method to super-resolve face images using a kernel principal component analysis-based prior model. A prior probability is formulated based on the energy lying outside the span of principal components identified in a higher-dimensional feature space. This is used to regularize the reconstruction of the high-resolution image. We demonstrate with experiments that including higher-order correlations results in significant improvements
Published in:
Multimedia, IEEE Transactions on
(Volume:9
,
Issue:
4
)
Date of Publication: June 2007
- Page(s):
- 888 - 892
- ISSN :
- 1520-9210
- INSPEC Accession Number:
- 9468571
- Digital Object Identifier :
- 10.1109/TMM.2007.893346
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 21 May 2007
- Issue Date :
- June 2007
- Sponsored by :
- IEEE Circuits & Systems Society
About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2013 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
