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Multimedia Tools and Applications

, Volume 74, Issue 6, pp 1997–2007 | Cite as

SISR via trained double sparsity dictionaries

  • Na Ai
  • Jinye Peng
  • Xuan Zhu
  • Xiaoyi Feng
Article

Abstract

In this paper we present an improved method for single image super-resolution (SISR). The improvement of our method is mainly attributed to the features that we used to train dictionary are wavelets of low resolution (LR) image(s) rather than the first and second derivatives as proposed by Zeyde et al. (2012). As a result, our trained dictionary pair has the property of double sparsity. That means our method can use relatively small training data set to obtain the dictionary with better adaptability to variant natural images. A number of comparison experiments on true images show our method achieves better generalization ability than that proposed in Zeyde et al. (2012).

Keywords

Single image super-resolution (SISR) Double sparsity Trained dictionary pair K-SVD algorithm 

Notes

Acknowledgments

This work was supported by Science Foundation of Northwest University (ND10010).

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.School of Electronics and InformationNorthwestern Polytechnic UniversityXianPeople’s Republic of China
  2. 2.School of Information and TechnologyNorthwest UniversityXianPeople’s Republic of China

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