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  Example-based Learning for Single-image Super-resolution and JPEG Artifact Removal

Kim, K., & Kwon, Y.(2008). Example-based Learning for Single-image Super-resolution and JPEG Artifact Removal (173). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

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Kim, KI1, 2, Author              
Kwon, Y, Author              
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1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: This paper proposes a framework for single-image super-resolution and JPEG artifact removal. The underlying idea is to learn a map from input low-quality images (suitably preprocessed low-resolution or JPEG encoded images) to target high-quality images based on example pairs of input and output images. To retain the complexity of the resulting learning problem at a moderate level, a patch-based approach is taken such that kernel ridge regression (KRR) scans the input image with a small window (patch) and produces a patchvalued output for each output pixel location. These constitute a set of candidate images each of which reflects different local information. An image output is then obtained as a convex combination of candidates for each pixel based on estimated confidences of candidates. 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 it has been done in existing example-based super-resolution 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 super-resolution and JPEG artifact removal methods shows the effectiveness of the proposed method. Furthermore, the proposed method is generic in that it has the potential to be applied to many other image enhancement applications.

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 Dates: 2008-08
 Publication Status: Published in print
 Pages: 28
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
 Table of Contents: -
 Rev. Type: -
 Identifiers: Report Nr.: 173
BibTex Citekey: 5324
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Title: Technical Report of the Max Planck Institute for Biological Cybernetics
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Pages: - Volume / Issue: 173 Sequence Number: - Start / End Page: - Identifier: -