dc.citation.conferencePlace |
GE |
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dc.citation.conferencePlace |
Munich |
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dc.citation.endPage |
465 |
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dc.citation.startPage |
456 |
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dc.citation.title |
30th DAGM Symposium on Pattern Recognition |
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dc.contributor.author |
Kim, Kwang In |
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dc.contributor.author |
Kwon, Younghee |
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dc.date.accessioned |
2023-12-20T04:37:09Z |
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dc.date.available |
2023-12-20T04:37:09Z |
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dc.date.created |
2019-03-04 |
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dc.date.issued |
2008-06-10 |
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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. |
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dc.identifier.bibliographicCitation |
30th DAGM Symposium on Pattern Recognition, pp.456 - 465 |
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dc.identifier.doi |
10.1007/978-3-540-69321-5_46 |
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dc.identifier.issn |
0302-9743 |
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dc.identifier.scopusid |
2-s2.0-54249130426 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/35793 |
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dc.identifier.url |
https://link.springer.com/chapter/10.1007%2F978-3-540-69321-5_46 |
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dc.language |
영어 |
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dc.publisher |
30th DAGM Symposium on Pattern Recognition |
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dc.title |
Example-Based Learning for Single-Image Super-Resolution |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2008-06-10 |
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