Double Adaptive Filtering of Gaussian Noise Degraded Images

  • Tuan D. Pham
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)

Abstract

Good estimate and simulation of the behavior of additive noise is central to the adaptive restoration of images corrupted with Gaussian noise. This paper presents a double adaptive filtering scheme in the sense that the filter is able to estimate the variance of additive noise in order to determine the filter gain for pixel updating, and also able to decide if the pixel should remain unfiltered. Experimental results obtained from the restoration of several images have shown the superiority of the proposed method to some benchmark image filters.

Keywords

Image restoration Gaussian noise adaptive filtering 

References

  1. 1.
    Awate, S.P., Whitaker, R.T.: Unsupervised, information-theoretic, adaptive image filtering for image restoration. IEEE Trans. PAMI 28, 364–376 (2006)Google Scholar
  2. 2.
    Barash, D., Comaniciu, D.: A Common Framework for Nonlinear Diffusion, Adaptive Smoothing, Bilateral Filtering and Mean Shift. Image Vision Computing 22, 73–81 (2004)CrossRefGoogle Scholar
  3. 3.
    Ferreira, V.C., Mascarenhas, N.D.A.: Analysis of the Robustness of Iterative Restoration Methods with Respect to Variations of the Point Spread Function. In: Proc. ICIP’00 III, pp. 789–792 (2000)Google Scholar
  4. 4.
  5. 5.
    Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing using Matlab. Pearson Prentice Hall, Englewood Cliffs (2004)Google Scholar
  6. 6.
    Journel, A.G., Huijbregts, C.J.: Mining Geostatistics. Academic Press, Chicago (1978)Google Scholar
  7. 7.
    Lee, J.-S.: IEEE Trans. PAMI 2, 165–168 (1980)Google Scholar
  8. 8.
    Lee, J.-S., Hoppel, K.: Noise modeling and estimation of remotely-sensed images. In: Proc. Int. Conf. Geoscience and Remote Sensing, vol. 2, pp. 1005–1008 (1989)Google Scholar
  9. 9.
    Lim, J.S.: Two-Dimensional Signal and Image Processing. Prentice-Hall, Englewood Cliffs (1990)Google Scholar
  10. 10.
    Pizurica, A., Philips, W., Lemahieu, I., Acheroy, M.: A joint inter and intrascale statistical model for Bayesian wavelet based image denoising. IEEE Trans. Image Processing 11, 545–557 (2002)CrossRefGoogle Scholar
  11. 11.
    Portilla, J., Strela, V., Wainwright, M., Simoncelli, E.: Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE Trans. Image Processing 12, 1338–1351 (2003)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Sanchez-Brea, L.M., Bernabeu, E.: On the standard deviation in charge-coupled device cameras: A variogram-based technique for nonuniform images. J. Electronic Imaging 11, 121–126 (2002)CrossRefGoogle Scholar
  13. 13.
    Sendur, L., Selesnick, I.: Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency. IEEE Trans. SP 50, 2744–2756 (2002)CrossRefGoogle Scholar
  14. 14.
    Wackernagel, H.: Multivariate Geostatistics. Springer, Heidelberg (2003)MATHGoogle Scholar
  15. 15.
    Wang, J.H., Liu, W.J., Lin, L.D.: Histogram-based fuzzy filter for image restoration. IEEE Trans. SMC - Part B: Cybernetics 32, 230–238 (2002)CrossRefGoogle Scholar
  16. 16.
    Xue, F., Liu, Q.S., Fan, W.H.: Iterative Image Restoration using a Non-Local Regularization Function and a Local Regularization Operator. In: Proc. ICPR’06 III, pp. 766–769 (2006)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Tuan D. Pham
    • 1
    • 2
  1. 1.Bioinformatics Applications Research Centre 
  2. 2.School of Mathematics, Physics, and Information Technology, James Cook University, Townsville, QLD 4811Australia

Personalised recommendations