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Single Textual Image Super-Resolution Using Multiple Learned Dictionaries Based Sparse Coding

  • Rim Walha
  • Fadoua Drira
  • Franck Lebourgeois
  • Christophe Garcia
  • Adel M. Alimi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8157)

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.

Keywords

Super-resolution sparse coding multiple learned dictionaries textual image 

References

  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Battiato, S., Gallo, G., Stanco, F.: Smart interpolation by anisotropic diffusion. In: ICIAP, pp. 572–577 (2003)Google Scholar
  3. 3.
    Ben-Ezra, M., Lin, Z.C., Wilburn, B.: Penrose pixels: Super-resolution in the detector layout domain. In: ICCV (2007)Google Scholar
  4. 4.
    Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic decomposition by basis pursuit. SIAM Review 43(1), 129–159 (2001)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Dalley, G., Freeman, W., Marks, J.: Single-frame text super-resolution: a bayesian approach. In: ICIP, pp. 3295–3298 (2004)Google Scholar
  6. 6.
    Freeman, W., Jones, T., Pasztor, E.: Example-based super-resolution. IEEE Computer Graphics and Applications 22(2), 55–65 (2002)CrossRefGoogle Scholar
  7. 7.
    Freeman, W.T., Pasztor, E.C., Carmichael, O.T.: Learning low-level vision. In: IJCV (2000)Google Scholar
  8. 8.
    Irani, M., Peleg, S.: Motion analysis for image enhancement: Resolution, occlusion and transparency. JVCL 4(4), 324–335 (1993)Google Scholar
  9. 9.
    Lee, H., Battle, A., Raina, R., Ng, A.Y.: Efficient sparse coding algorithms. In: Advances in Neural Information Processing Systems (NIPS) (2007)Google Scholar
  10. 10.
    Li, X., Orchard, M.T.: New edge-directed interpolation. IEEE Trans. Image Process. 10, 1521–1527 (2001)CrossRefGoogle Scholar
  11. 11.
    Luong, H., Philips, W.: Non-local text image reconstruction. In: ICDAR, vol. 1, pp. 546–550 (2007)Google Scholar
  12. 12.
    Luong, H., Philips, W.: Robust reconstruction of low-resolution document images by exploiting repetitive character behavior. IJDAR 11(1), 39–51 (2008)CrossRefGoogle Scholar
  13. 13.
    Mirkin, B.G.: Clustering for data mining: a data recovery approach, vol. 3. CRC Press (2005)Google Scholar
  14. 14.
    Mairal, J., Sapiro, G., Elad, M.: Learning multiscale sparse representations for image and video restoration. SIAM Multiscale Model. Simul. 7(1), 214–241 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  15. 15.
    Staelin, C., Greig, D., Fischer, M., Maurer, R.: Neural network image scaling using spatial errors. In: Tech. Rep. HPL-2003-26R1, HP Labs (2003)Google Scholar
  16. 16.
    Sun, J., Xu, Z., Shum, H.: Image super-resolution using gradient profile prior. In: CVPR (2008)Google Scholar
  17. 17.
    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)Google Scholar
  18. 18.
    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)CrossRefGoogle Scholar
  19. 19.
    Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010)CrossRefMathSciNetGoogle Scholar
  20. 20.
    Yang, J., Wright, J., Huang, T., Ma, Y.: Image Super-Resolution as Sparse Representation of Raw Image Patches. In: CVPR (2008)Google Scholar
  21. 21.
    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)Google Scholar
  22. 22.
    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)CrossRefGoogle Scholar
  23. 23.

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Rim Walha
    • 1
    • 2
  • Fadoua Drira
    • 1
  • Franck Lebourgeois
    • 2
  • Christophe Garcia
    • 2
  • Adel M. Alimi
    • 1
  1. 1.ENIS, REGIMUniversity of SfaxSfaxTunisia
  2. 2.INSA-Lyon, CNRS, LIRIS, UMR5205University of LyonFrance

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