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Conference Paper

Example-Based Learning for Single-Image Super-Resolution


Kim,  KI
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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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.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C8FB-F
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.