Abstract
A new algorithm for single-image super-resolution based on selective sparse representation over a set of coupled dictionary pairs is proposed. Patch sharpness measure for high- and low-resolution patch pairs defined via the magnitude of the gradient operator is shown to be approximately invariant to the patch resolution. This measure is employed in the training stage for clustering the training patch pairs and in the reconstruction stage for model selection. For each cluster, a pair of low- and high-resolution dictionaries is learned. In the reconstruction stage, the sharpness measure of a low-resolution patch is used to select the cluster it belongs to. The sparse coding coefficients of the patch over the selected low-resolution cluster dictionary are calculated. The underlying high-resolution patch is reconstructed by multiplying the high-resolution cluster dictionary with the calculated coefficients. The performance of the proposed algorithm is tested over a set of natural images. PSNR and SSIM results show that the proposed algorithm is competitive with the state-of-the-art super-resolution algorithms. In particular, it significantly out-performs the state-of-the-art algorithms for images with sharp edges and corners. Visual comparison results also support the quantitative results.




Similar content being viewed by others
References
Elad, M., Aharon, M.: Image denoising via learned dictionaries and sparse representation. In: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, 895–900 (2006)
Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution as sparse representation of raw image patches. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2008)
Skretting, K., Engan, K.: Image compression using learned dictionaries by RLS-DLA and compared with K-SVD. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1517–1520 (2011)
Wright, J., Ma, Y., Mairal, J., Sapiro, G., Huang, T.S., Yan, S.: Sparse representation for computer vision and pattern recognition. Proc. IEEE 98(6), 1031–1044 (2010)
Rubinstein, R., Alfred, M.B., Elad, M.: Dictionaries for sparse representation modeling. Proc. IEEE 98(6), 1045–1057 (2010)
Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19, 2861–2873 (2010)
Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse representations. Curves Surf. 6920, 711–730 (2010)
Dong, W., Zhang, L., Shi, G., Wu, X.: Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Trans. Image Process. 20(7), 1838–1857 (2011)
Thanou, D., Shuman, D.I., Frossard, P.: Parametric dictionary learning for graph signals. In: Proceedings of the IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 487–490 (2013)
Mallat, S., Yu, G.: Super-resolution with sparse mixing estimators. IEEE Trans. Image Process. 19(11), 2889–2900 (2010)
Feng, J., Song, L., Yang, X., Zhang, W.: Learning dictionary via subspace segmentation for sparse representation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (ICIP), pp. 1245–1248 (2011)
Yu, G., Sapiro, G., Mallat, S.: Image modeling and enhancement via structured sparse model selection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (ICIP), pp. 1641–1644 (2010)
Kumar, J., Chen, F., Doermann, D.: Sharpness estimation for document and scene images. In: 21st International Conference on Pattern Recognition (ICPR), pp. 3292–3295 (2012)
He, L., Qi, H., Zaretzki, R.: Beta process joint dictionary learning for coupled feature spaces with application to single image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 345–352 (2013)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice-Hall Inc, Englewood Cliffs, NJ (2002)
Sun, J., Xu, Z., Shum, H.Y.: Image super-resolution using gradient profile prior. In: IEEE Conference on Computer Vision and Pattern Recognition (ICIP), pp. 1–8 (2008)
http://see.xidian.edu.cn/faculty/wsdong/wsdong_downloads.htm
Aharon, M., Elad, M., Bruckstein, A.M.: The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representations. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)
Peleg, T., Elad, M.: A statistical prediction model based on sparse representations for single image super-resolution. IEEE Trans. Image Process. 23(6), 2569–2582 (2014)
Elad, M.: Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. Springer, Berlin (2010)
Kodak lossless true color image suite. http://r0k.us/graphics/kodak/
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 416–423 (2001)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yeganli, F., Nazzal, M., Unal, M. et al. Image super-resolution via sparse representation over multiple learned dictionaries based on edge sharpness. SIViP 10, 535–542 (2016). https://doi.org/10.1007/s11760-015-0771-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-015-0771-7