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



ID: 548389.0, MPI für biologische Kybernetik / Biologische Kybernetik
Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior
Authors:Kim, K.I.; Kwon, Y.
Date of Publication (YYYY-MM-DD):2010-06
Title of Journal:IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume:32
Issue / Number:6
Start Page:1127
End Page:1133
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:This paper proposes a framework for single-image super-resolution. The underlying idea is to learn a map from input low-resolution images to target high-resolution images based on example pairs of input and output images. Kernel ridge regression (KRR) is adopted for this purpose. To reduce the time complexity of training and testing for KRR, a sparse solution is found by combining the ideas of kernel matching pursuit and gradient descent. As a regularized solution, KRR leads to a better generalization than simply storing the examples as has been done in existing example-based algorithms and results in much less noisy images. However, this may introduce blurring and ringing artifacts around major edges as sharp changes are penalized severely. A prior model of a generic image class which takes into account the discontinuity property of images is adopted to resolve this problem. Comparison with existing algorithms shows the effectiveness of the proposed method.
External Publication Status:published
Document Type:Article
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:6523
URL:http://ieeexplore.ieee.org/Xplore/defdeny.jsp?url=...
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