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Using Shear Invariant for Image Denoising in the Contourlet Domain

  • Jian Jia
  • Licheng Jiao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4153)

Abstract

A new contourlet transform based on shear invariant is proposed for image denoising. Image denoising by means of the contourlet transform(CT) introduces many visual artifacts due to the Gibbs-like phenomena. Due to the lack of transform invariance of the contourlet transform, we employ a shear technique to develop shear invariant contourlet denoising scheme (SICT). This scheme achieves enhanced estimation results for images that are corrupted with additive Gaussian noise over a wide range of noise variance. Experiments show that the proposed approach outperforms the translation invariant wavelets method and translation invariant contourlets method both visually and in terms of the PSNR values at most cases. Especially, SICT yields better visual results even has worse PSNR result than translation invariant contourlet transform.

Keywords

Translation Invariant Image Denoising Hard Thresholding Laplacian Pyramid Contourlet Transform 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Po, D.D., Do, M.N.: Directional multiscale modeling of images using the contourlet transform. In: 2003 IEEE Workshop on Statistical Signal Processing, September 28 - October 1, pp. 262–265 (2003)Google Scholar
  2. 2.
    Eslami, R., Radha, H.: Image Denoising Using Translation Invariant Contourlet Transform. In: Acoustics, Speech, and Signal Processing, 2005. Proceedings (ICASSP 2005). IEEE International Conference, pp. 557–560 (2005)Google Scholar
  3. 3.
    Candès, E.J., Donoho, D.L.: Curvelets - A Surprisingly Effective Nonadaptive Representation for Objects with Edges. In: Schumaker, L.L., et al. (eds.) Curves and Surfaces, Vanderbilt University Press, Nashville (1999)Google Scholar
  4. 4.
    Do, M.N., Vetterli, M.: The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans. on Image Pmrersing 14(12), 2091–2106 (2005)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Arthur, L., da Cunha, M.N.: Do: Bi-Orthogonal Filter Banks with Directional Vanishing Moments [image representation applications]. In: Acoustics, Speech, and Signal Processing, 2005. Proceedings (ICASSP 2005). IEEE International Conference, March 18-23, 2005, vol. 4, pp. 553–556 (2005)Google Scholar
  6. 6.
    Do, M.N., Vetterli, M.: Pyramidal directional filter banks and curvelets. In: Proc. of IEEE International Conference on Image Processing (ICIP), October 7-10, 2001, vol. 3, pp. 158–161 (2001)Google Scholar
  7. 7.
    Do, M.N., Vetterli, M.: Contourlets: a directional multiresolution image representation. In: Image Processing, 2002, Proceedings. 2002 International Conference, September 2002, vol. 1, pp. 357–360 (2002)Google Scholar
  8. 8.
    Do, M. N.: Contourlet Toolbox at, http://www.ifp.uiuc.edu/~minhdo/software/
  9. 9.
    Eslami, R., Radha, H.: The Contourlet Transform for Image De-noising Using Cycle Spinning. In: Signals, Systems & Computers, 2003, Conference Record of the Thirty-Seventh Asilomar Conference, November 9-12, 2003, vol. 2, pp. 1982–1986 (2003)Google Scholar
  10. 10.
    Donoho, D.L.: Wavelab802 at, http://www-stat.stanford.edu/~wavelab/
  11. 11.
    Stéphane Mallat, A.: Wavelet Tour of Signal Processing. Academic Press, London (1999)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jian Jia
    • 1
    • 2
  • Licheng Jiao
    • 1
  1. 1.Institute of Intelligent Information ProcessingXidian UniversityXi’an, ShaanxiChina
  2. 2.Department of MathematicsNorthwest UniversityXi’an, ShaanxiChina

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