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Kim, Kwang In
Machine Learning and Vision Lab.
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Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior

Author(s)
Kim, Kwang InKwon, Younghee
Issued Date
2010-06
DOI
10.1109/TPAMI.2010.25
URI
https://scholarworks.unist.ac.kr/handle/201301/26213
Fulltext
https://ieeexplore.ieee.org/document/5396341
Citation
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v.32, no.6, pp.1127 - 1133
Abstract
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.
Publisher
IEEE COMPUTER SOC
ISSN
0162-8828
Keyword (Author)
Computer visionmachine learningimage enhancementdisplay algorithms

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