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Example-Based Learning for Single-Image Super-Resolution


Conference Paper


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.

Author(s): Kim, KI. and Kwon, Y.
Book Title: DAGM 2008
Journal: Pattern Recognition: Proceedings of the 30th DAGM Symposium
Pages: 456-463
Year: 2008
Month: June
Day: 0
Editors: Rigoll, G.
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1007/978-3-540-69321-5_46
Event Name: 30th Annual Symposium of the German Association for Pattern Recognition
Event Place: München, Germany

Address: Berlin, Germany
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Example-Based Learning for Single-Image Super-Resolution},
  author = {Kim, KI. and Kwon, Y.},
  journal = {Pattern Recognition: Proceedings of the 30th DAGM Symposium},
  booktitle = {DAGM 2008},
  pages = {456-463},
  editors = {Rigoll, G. },
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = jun,
  year = {2008},
  month_numeric = {6}