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Optical Review

, Volume 20, Issue 6, pp 491–495 | Cite as

Image restoration with dual-prior constraint models based on Split Bregman

  • Li Ao
  • Li Yibing
  • Yang Xiaodong
  • Liu Yue
Regular Paper

Abstract

In order to utilizing the local and non-local information in the image, we proposed a novel sparse scheme for image restoration in this paper. The new scheme includes two important contributions. The first one is that we extended the image prior model in conventional total variation to the dual-prior models for combining the local smoothness and non-local sparsity under regularization framework. The second one is we developed a modified iterative Split Bregman majorization method to solve the objective function with dual-prior models. The experimental results show that the proposed scheme achieved the state-of-the-art performance compared to the current restoration algorithms.

Keywords

image restoration non-local sparsity Split Bregman total variation regularization 

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References

  1. 1).
    M. Elad: Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing (Springer, New York, 2010).CrossRefGoogle Scholar
  2. 2).
    L. I. Rudin, S. Osher, and E. Fatemi: Physica D 60 (1992) 259.ADSCrossRefzbMATHGoogle Scholar
  3. 3).
    J. M. Bioucas-Dias, M. A. T. Figueiredo, and J. P. Oliveira: IEEE Int. Conf. Acoustic, Speech and Signal Processing, 2006, p. 861.Google Scholar
  4. 4).
    M. Zhu, S. J. Wright, and T. F. Chan: Comput. Appl. Math. Rep. 47 (2010) 377.MathSciNetzbMATHGoogle Scholar
  5. 5).
    M. Sun, N. Feng, Y. Shen, J. Li, L. Ma, and Z. Wu: Chin. Opt. Lett. 9 (2011) 061002.CrossRefGoogle Scholar
  6. 6).
    M. Elad and M. Aharon: IEEE Trans. Image Process. 15 (2006) 3736.MathSciNetADSCrossRefGoogle Scholar
  7. 7).
    J. A. Tropp: IEEE Trans. Inf. Theory 50 (2004) 2231.MathSciNetCrossRefGoogle Scholar
  8. 8).
    A. Beck and M. Teboulle: IEEE Trans. Image Process. 18 (2009) 2419.MathSciNetADSCrossRefGoogle Scholar
  9. 9).
    J. Dahl, P. C. Hansen, S. H. Jensen, and T. L. Jensen: Numer. Algorithms 53 (2010) 67.MathSciNetADSCrossRefzbMATHGoogle Scholar
  10. 10).
    S. S. Chen, D. L. Donoho, and M. A. Saunders: SIAM J. Sci. Comput. 20 (1998) 33.MathSciNetCrossRefGoogle Scholar
  11. 11).
    J. K. Romberg: Annu. Conf. Information Science and Systems, 2006, p. 213.Google Scholar
  12. 12).
    Y. Nesterov: ECORE Discussion Paper 76 (2007).Google Scholar
  13. 13).
    T. Goldstein and S. Osher: SIAM J. Image Sci. 2 (2009) 323.MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14).
    J. Zhang, Z.-H. Wei, and L. Xiao: Intell. Comput. Theories Appl. 7390 (2012) 189.CrossRefGoogle Scholar
  15. 15).
    D. L. Donoho and I. M. Johnstone: Ann. Stat. 26 (1998) 879.MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16).
    J. Friedman, T. Hastie, H. Höfling, and R. Tibshirani: Ann. Appl. Stat. 1 (2007) 302.MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17).
    A. L. da Cunha, J. Zhou, and M. N. Do: IEEE Trans. Image Process. 15 (2006) 3089.ADSCrossRefGoogle Scholar
  18. 18).
    P. Chatterjee and P. Milanfar: IEEE Trans. Image Process. 18 (2009) 1438.MathSciNetADSCrossRefGoogle Scholar
  19. 19).
    A. Buades, B. Coll, and J.-M. Morel: IEEE Conf. Computer Vision and Pattern Recognition, 2005, p. 60.Google Scholar

Copyright information

© The Optical Society of Japan 2013

Authors and Affiliations

  1. 1.Institute of Information and Communication EngineeringHarbin Engineering UniversityHarbinChina

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