Referenced Items are not available for this document.
We propose a framework for expanding a given image using an interpolator that is trained in advance with training data, based on sparse Bayesian estimation for determining the optimal and compact support for efficient image expansion. Experiments on test data show that learned interpolators are compact yet superior to classical ones.
Published in:
Image Processing, IEEE Transactions on
(Volume:19
,
Issue:
6
)
Date of Publication: June 2010
- Page(s):
- 1480 - 1490
- ISSN :
- 1057-7149
- INSPEC Accession Number:
- 11292947
- Digital Object Identifier :
- 10.1109/TIP.2010.2043010
- Product Type:
- Journals & Magazines
- Date of Publication :
- 08 March 2010
- Date of Current Version :
- 13 May 2010
- Issue Date :
- June 2010
- Sponsored by :
- IEEE Signal Processing Society
- PubMed ID :
- 20215080
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