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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 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.

Author(s): Kim, KI. and Kwon, Y.
Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume: 32
Number (issue): 6
Pages: 1127-1133
Year: 2010
Month: June
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1109/TPAMI.2010.25
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web


  title = {Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior},
  author = {Kim, KI. and Kwon, Y.},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume = {32},
  number = {6},
  pages = {1127-1133},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jun,
  year = {2010},
  month_numeric = {6}