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Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior
June 2010 (vol. 32 no. 6)
pp. 1127-1133
Kwang In Kim, Max-Planck-Institut für biologische Kybernetik Spemannstr, Tübingen
Younghee Kwon, KAIST, Daejeon
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.

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Index Terms:
Computer vision, machine learning, image enhancement, display algorithms.
Citation:
Kwang In Kim, Younghee Kwon, "Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 6, pp. 1127-1133, June 2010, doi:10.1109/TPAMI.2010.25
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