Circuits, Systems, and Signal Processing

, Volume 33, Issue 2, pp 629–642 | Cite as

Edge-Preserving Regularized Filter with Spatial Local Outlier Measure and Q-Estimate

  • Zhu Zhu
  • Xiaoguo Zhang
  • Qing Wang
  • Xueyin Wan
  • Yanchang Xiao
Short Paper
  • 374 Downloads

Abstract

This paper presents an efficient random-valued impulse noise removal algorithm. The filtering process contains two phases: a detection phase followed by a filtering phase. In the detection phase, the proposed method uses the novel image statistics, the spatial local outlier measure (SLOM) and the Q-estimate, to identify impulses in a corrupted image. When the noise pixels are identified, their values are restored by an edge-preserving regularized method in the filtering phase. Extensive experimental results show that our filter provides a significant improvement over many other existing techniques.

Keywords

Random-valued impulse noise Spatial local outlier measure Q-Estimate Edge-preserving regularized method 

Notes

Acknowledgements

The authors acknowledge the support of The National Key Technologies R&D Program of China during the 12th Five-Year Period (No. 2012BAJ23B02).

References

  1. 1.
    S. Akkoul, R. Lédée, R. Leconge, R. Harba, A new adaptive switching median filter. IEEE Signal Process. Lett. 17(6), 587–590 (2010) CrossRefGoogle Scholar
  2. 2.
    A.S. Awad, Standard deviation for obtaining the optimal direction in removal of impulse noise. IEEE Signal Process. Lett. 18(7), 407–410 (2011) CrossRefGoogle Scholar
  3. 3.
    J.F. Cai, R.H. Chan, B. Morini, Minimization of a detail-preserving regularization functional for impulse noise removal. J. Math. Imaging Vis. 29(1), 79–91 (2007) CrossRefGoogle Scholar
  4. 4.
    T. Chen, H.R. Wu, Adaptive impulse detection using center-weighted median filters. IEEE Signal Process. Lett. 8(1), 1–3 (2001) CrossRefGoogle Scholar
  5. 5.
    Y. Dong, R.H. Chan, S. Xu, A detection statistic for random-valued impulse noise. IEEE Trans. Image Process. 16(4), 1112–1120 (2007) CrossRefMathSciNetGoogle Scholar
  6. 6.
    Y. Dong, S. Xu, A new directional weighted median filter for removal of random-valued impulse noise. IEEE Signal Process. Lett. 14(3), 193–196 (2007) CrossRefGoogle Scholar
  7. 7.
    R. Garnett, T. Huegerich, C. Chui, W. He, A universal noise removal algorithm with an impulse detector. IEEE Trans. Image Process. 14(11), 1747–1754 (2005) CrossRefGoogle Scholar
  8. 8.
    U. Ghanekar, A.K. Singh, R. Pandey, The contrast enhancement-based filter for removal of random valued impulse noise. IEEE Signal Process. Lett. 17(1), 47–50 (2010) CrossRefGoogle Scholar
  9. 9.
    R.C. Gonzalez, R.E. Woods, Digital Image Processing (Prentice-Hall, Englewood Cliffs, 2002) Google Scholar
  10. 10.
    W. Luo, A new efficient impulse detection algorithm for the removal of impulse noise. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 88-A(10), 2579–2586 (2005) CrossRefGoogle Scholar
  11. 11.
    M. Nikolova, A variational approach to remove outliers and impulse noise. J. Math. Imaging Vis. 20(1–2), 99–120 (2004) CrossRefMathSciNetGoogle Scholar
  12. 12.
    P.J. Rousseeuw, C. Croux, Alternatives to the median absolute deviation. J. Am. Stat. Assoc. 88(424), 1273–1283 (1993) CrossRefMATHMathSciNetGoogle Scholar
  13. 13.
    P. Sun, S. Chawla, SLOM: a new measure for local spatial outlier. Knowl. Inf. Syst. 9(4), 412–429 (2006) CrossRefGoogle Scholar
  14. 14.
    X. Zeng, L. Yang, Mixed impulse and Gaussian noise removal using detail-preserving regularization. Opt. Eng. 49(9), 097002 (2010) CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Zhu Zhu
    • 1
  • Xiaoguo Zhang
    • 2
  • Qing Wang
    • 1
  • Xueyin Wan
    • 1
  • Yanchang Xiao
    • 1
  1. 1.School of Instrument Science and EngineeringSoutheast UniversityNanjingChina
  2. 2.Suzhou Research InstituteSoutheast UniversitySuzhouChina

Personalised recommendations