Abstract
In this paper, we propose a new approach based on sparse coding for single textual image Super-Resolution (SR). The proposed approach is able to build more representative dictionaries learned from a large training Low-Resolution/High-Resolution (LR/HR) patch pair database. In fact, an intelligent clustering is employed to partition such database into several clusters from which multiple coupled LR/HR dictionaries are constructed. Based on the assumption that patches of the same cluster live in the same subspace, we exploit for each local LR patch its similarity to clusters in order to adaptively select the appropriate learned dictionary over that such patch can be well sparsely represented. The obtained sparse representation is hence applied to generate a local HR patch from the corresponding HR dictionary. Experiments on textual images show that the proposed approach outperforms its counterparts in visual fidelity as well as in numerical measures.
Chapter PDF
Similar content being viewed by others
References
Aharon, M., Elad, M., Bruckstein, A.: K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)
Battiato, S., Gallo, G., Stanco, F.: Smart interpolation by anisotropic diffusion. In: ICIAP, pp. 572–577 (2003)
Ben-Ezra, M., Lin, Z.C., Wilburn, B.: Penrose pixels: Super-resolution in the detector layout domain. In: ICCV (2007)
Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic decomposition by basis pursuit. SIAM Review 43(1), 129–159 (2001)
Dalley, G., Freeman, W., Marks, J.: Single-frame text super-resolution: a bayesian approach. In: ICIP, pp. 3295–3298 (2004)
Freeman, W., Jones, T., Pasztor, E.: Example-based super-resolution. IEEE Computer Graphics and Applications 22(2), 55–65 (2002)
Freeman, W.T., Pasztor, E.C., Carmichael, O.T.: Learning low-level vision. In: IJCV (2000)
Irani, M., Peleg, S.: Motion analysis for image enhancement: Resolution, occlusion and transparency. JVCL 4(4), 324–335 (1993)
Lee, H., Battle, A., Raina, R., Ng, A.Y.: Efficient sparse coding algorithms. In: Advances in Neural Information Processing Systems (NIPS) (2007)
Li, X., Orchard, M.T.: New edge-directed interpolation. IEEE Trans. Image Process. 10, 1521–1527 (2001)
Luong, H., Philips, W.: Non-local text image reconstruction. In: ICDAR, vol. 1, pp. 546–550 (2007)
Luong, H., Philips, W.: Robust reconstruction of low-resolution document images by exploiting repetitive character behavior. IJDAR 11(1), 39–51 (2008)
Mirkin, B.G.: Clustering for data mining: a data recovery approach, vol. 3. CRC Press (2005)
Mairal, J., Sapiro, G., Elad, M.: Learning multiscale sparse representations for image and video restoration. SIAM Multiscale Model. Simul. 7(1), 214–241 (2008)
Staelin, C., Greig, D., Fischer, M., Maurer, R.: Neural network image scaling using spatial errors. In: Tech. Rep. HPL-2003-26R1, HP Labs (2003)
Sun, J., Xu, Z., Shum, H.: Image super-resolution using gradient profile prior. In: CVPR (2008)
Walha, R., Drira, F., Lebourgeois, F., Alimi, A.M.: Super-resolution of single text image by sparse representation. In: Proc. of Workshop on Document Analysis and Recognition, pp. 22–29 (2012)
Wang, Z., Bovik, A.C., Sheikh, H.R.: Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010)
Yang, J., Wright, J., Huang, T., Ma, Y.: Image Super-Resolution as Sparse Representation of Raw Image Patches. In: CVPR (2008)
Yang, S., Wang, M., Chen, Y., Sun, Y.: Single-Image Super-Resolution Reconstruction via Learned Geometric Dictionaries and Clustered Sparse Coding. IEEE Trans. Image Process. 21(9) (2012)
Yang, S.Y., Liu, Z.Z., Jiao, L.C.: Multitask dictionary learning and sparse representation based single-image super-resolution reconstruction. Neurocomputing 74(17), 3193–3203 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Walha, R., Drira, F., Lebourgeois, F., Garcia, C., Alimi, A.M. (2013). Single Textual Image Super-Resolution Using Multiple Learned Dictionaries Based Sparse Coding. In: Petrosino, A. (eds) Image Analysis and Processing – ICIAP 2013. ICIAP 2013. Lecture Notes in Computer Science, vol 8157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41184-7_45
Download citation
DOI: https://doi.org/10.1007/978-3-642-41184-7_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41183-0
Online ISBN: 978-3-642-41184-7
eBook Packages: Computer ScienceComputer Science (R0)
