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).
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bruckstein AM, Donoho DL, Elad M (2009) From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Rev 51(1):34–81
Elad M (2010) Sparse and redundant representations: from theory to applications in signal and image processing. Springer, Heidelberg
Marquina A, Osher SJ (2008) Image super-resolution by TV-regularization and Bregman iteration. J Sci Comput 37(3):367–382
Rubinstein R, Bruckstein AM, Elad M (2010) Dictionaries for sparse representation modeling. Proc IEEE 98(6):1045–1057
Rubinstein R, Zibulevsky M, Elad M (2010) Double sparsity: learning sparse dictionaries for sparse signal approximation. IEEE Trans Signal Proc 58(3):1553–1564
Yang JC, Wright J, Huang TS, Ma Y (2008) Image super-resolution as sparse representation of raw image patches. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1–8
Yang JC, Wright J, Huang TS, Ma Y (2010) Image super resolution via sparse representation. IEEE Trans Image Process 19(11):2861–2873
Zeyde R, Elad M, Protter M (2012) On single image scale-up using sparse representations. Curves and Surfaces, LNCS 6920, 711–730, Springer, Heidelberg
Zhang L, Xiaolin W (2006) An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans Image Process 15(8):2226–2238
Acknowledgments
This work was supported by Science Foundation of Northwest University (ND10010).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ai, N., Peng, J., Zhu, X. et al. SISR via trained double sparsity dictionaries. Multimed Tools Appl 74, 1997–2007 (2015). https://doi.org/10.1007/s11042-013-1736-x
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-013-1736-x