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)


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.


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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  1. 1.
    Baker, S., Kanade, T.: Limits on super-resolution and how to break them. IEEE Trans. Pattern Analysis and Machine Intelligence 24(9), 1167–1183 (2002)CrossRefGoogle Scholar
  2. 2.
    Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. IEEE Computer Graphics and Applications 22(2), 56–65 (2002)CrossRefGoogle Scholar
  3. 3.
    Hertzmann, A., Jacobs, C.E., Oliver, N., Curless, B., Salesin, D.H.: Image analogies. In: Computer Graphics (Proc. Siggraph 2001), pp. 327–340. ACM Press, New York (2001)Google Scholar
  4. 4.
    Keerthi, S.S., Chu, W.: A matching pursuit approach to sparse gaussian process regression. In: Advances in Neural Information Processing Systems, MIT Press, Cambridge (2005)Google Scholar
  5. 5.
    Kim, K.I., Franz, M.O., Schölkopf, B.: Iterative kernel principal component analysis for image modeling. IEEE Trans. Pattern Analysis and Machine Intelligence 27(9), 1351–1366 (2005)CrossRefGoogle Scholar
  6. 6.
    Kim, K.I., Kim, D.H., Kim, J.H.: Example-based learning for image super-resolution. In: Proc. the third Tsinghua-KAIST Joint Workshop on Pattern Recognition, pp. 140–148 (2004)Google Scholar
  7. 7.
    Ni, K., Nguyen, T.Q.: Image superresolution using support vector regression. IEEE Trans. Image Processing 16(6), 1596–1610 (2007)CrossRefGoogle Scholar
  8. 8.
    Snelson, E., Ghahramani, Z.: Sparse gaussian processes using pseudo-inputs. In: Advances in Neural Information Processing Systems, MIT Press, Cambridge (2006)Google Scholar
  9. 9.
    Tappen, M.F., Russel, B.C., Freeman, W.T.: Exploiting the sparse derivative prior for super-resolution and image demosaicing. In: Proc. IEEE Workshop on Statistical and Computational Theories of Vision (2003)Google Scholar
  10. 10.
    Tschumperlé, D., Deriche, R.: Vector-valued image regularization with pdes: a common framework for different applications. IEEE Trans. Pattern Analysis and Machine Intelligence 27(4), 506–517 (2005)CrossRefGoogle Scholar
  11. 11.
    Vincent, P., Bengio, Y.: Kernel matching pursuit. Machine Learning 48, 165–187 (2002)CrossRefzbMATHGoogle Scholar

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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