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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 420017.0, MPI für biologische Kybernetik / Biologische Kybernetik
Example-Based Learning for Single-Image Super-Resolution
Authors:Kim, K.I.; Kwon, Y.
Editors:Rigoll, G.
Date of Publication (YYYY-MM-DD):2008-06
Title of Proceedings:Pattern Recognition: Proceedings of the 30th DAGM Symposium
Start Page:456
End Page:463
Physical Description:8
Audience:Not Specified
Intended Educational Use:No
Abstract / Description: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.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI f�r biologische Kybernetik/Empirical Inference (Dept. Sch�lkopf)
Identifiers:LOCALID:5091
URL:http://www.springerlink.com/content/k6x51700654332...
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