Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior

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...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2010-06, Vol.32 (6), p.1127-1133
Main Authors: Kim, Kwang In, Kwon, Younghee
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Computer vision
Data processing. List processing. Character string processing
Discontinuity
display algorithms
Displays
Energy resolution
Exact sciences and technology
Image enhancement
Image resolution
Kernel
Kernels
Machine learning
Machine learning algorithms
Matching pursuit algorithms
Mathematical models
Memory organisation. Data processing
Pattern analysis
Pattern recognition. Digital image processing. Computational geometry
Product introduction
Regression
Ridges
Software
Spatial resolution
Studies
Testing
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Summary: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.
ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.2010.25