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DAGM
2008
Springer

Example-Based Learning for Single-Image Super-Resolution

14 years 16 days ago
Example-Based Learning for Single-Image Super-Resolution
Abstract. This paper proposes a regression-based method for singleimage 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 postprocessed using a prior model of a generic image class. Experimental results demonstrate the effectiveness of the proposed method.
Kwang In Kim, Younghee Kwon
Added 19 Oct 2010
Updated 19 Oct 2010
Type Conference
Year 2008
Where DAGM
Authors Kwang In Kim, Younghee Kwon
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