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Example-Based Learning for Single-Image Super-Resolution

  • Kwang In Kim
  • Younghee Kwon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5096)

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.

Keywords

Gradient Descent Support Vector Regression Near Neighbor Image Patch Kernel Principal Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Kwang In Kim
    • 1
  • Younghee Kwon
    • 2
  1. 1.Max-Planck-Institute für biologische KybernetikTübingenGermany
  2. 2.Korea Advanced Institute of Science and TechnologyTaejonKorea

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