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  Example-Based Learning for Single-Image Super-Resolution

Kim, K., & Kwon, Y. (2008). Example-Based Learning for Single-Image Super-Resolution. In G. Rigoll (Ed.), Pattern Recognition: 30th DAGM Symposium Munich, Germany, June 10-13, 2008 (pp. 456-463). Berlin, Germany: Springer.

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 Creators:
Kim, KI1, 2, Author              
Kwon, Y, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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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 Dates: 2008-06
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-540-69321-5_46
BibTex Citekey: 5091
 Degree: -

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Title: 30th Annual Symposium of the German Association for Pattern Recognition (DAGM 2008)
Place of Event: München, Germany
Start-/End Date: 2008-06-10 - 2008-06-13

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Title: Pattern Recognition: 30th DAGM Symposium Munich, Germany, June 10-13, 2008
Source Genre: Proceedings
 Creator(s):
Rigoll, G, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 456 - 463 Identifier: ISBN: 978-3-540-69320-8

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Publ. Info: -
Pages: - Volume / Issue: 5096 Sequence Number: - Start / End Page: - Identifier: -