DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim K.I. | ko |
dc.contributor.author | Kwon Y. | ko |
dc.date.accessioned | 2013-03-27T00:39:09Z | - |
dc.date.available | 2013-03-27T00:39:09Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 2008-06-10 | - |
dc.identifier.citation | 30th DAGM Symposium on Pattern Recognition, pp.456 - 465 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10203/157569 | - |
dc.description.abstract | This paper proposes a regression-based method for single-image super-resolution. Kernel ridge regression (KRR) is used to estimate the high-frequency details of the underlying high-resolution image. A sparse solution of KRR is found by combining the ideas of kernel matching pursuit and gradient descent, which allows time-complexity to be kept to a moderate level. To resolve the problem of ringing artifacts occurring due to the regularization effect, the regression results are post-processed using a prior model of a generic image class. Experimental results demonstrate the effectiveness of the proposed method. | - |
dc.language | English | - |
dc.publisher | 30th DAGM Symposium on Pattern Recognition | - |
dc.title | Example-based learning for single-image super-resolution | - |
dc.type | Conference | - |
dc.identifier.wosid | 000256932900046 | - |
dc.identifier.scopusid | 2-s2.0-54249130426 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 456 | - |
dc.citation.endingpage | 465 | - |
dc.citation.publicationname | 30th DAGM Symposium on Pattern Recognition | - |
dc.identifier.conferencecountry | GE | - |
dc.identifier.conferencelocation | Munich | - |
dc.identifier.doi | 10.1007/978-3-540-69321-5_46 | - |
dc.contributor.localauthor | Kwon Y. | - |
dc.contributor.nonIdAuthor | Kim K.I. | - |
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