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  Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior

Kim, K., & Kwon, Y. (2010). Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(6), 1127-1133. doi:10.1109/TPAMI.2010.25.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BF84-2 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-75C2-F
Genre: Journal Article

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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 framework for single-image super-resolution. The underlying idea is to learn a map from input low-resolution images to target high-resolution images based on example pairs of input and output images. Kernel ridge regression (KRR) is adopted for this purpose. To reduce the time complexity of training and testing for KRR, a sparse solution is found by combining the ideas of kernel matching pursuit and gradient descent. As a regularized solution, KRR leads to a better generalization than simply storing the examples as has been done in existing example-based algorithms and results in much less noisy images. However, this may introduce blurring and ringing artifacts around major edges as sharp changes are penalized severely. A prior model of a generic image class which takes into account the discontinuity property of images is adopted to resolve this problem. Comparison with existing algorithms shows the effectiveness of the proposed method.

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 Dates: 2010-06
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1109/TPAMI.2010.25
BibTex Citekey: 6523
 Degree: -

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Title: IEEE Transactions on Pattern Analysis and Machine Intelligence
  Other : IEEE Trans. Pattern Anal. Mach. Intell.
Source Genre: Journal
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Publ. Info: New York : IEEE Computer Society.
Pages: - Volume / Issue: 32 (6) Sequence Number: - Start / End Page: 1127 - 1133 Identifier: ISSN: 0162-8828
CoNE: https://pure.mpg.de/cone/journals/resource/954925479551