A STATISTICAL BASED APPROACH FOR REMOVING HEAVY TAIL NOISE FROM IMAGES

M. El Hassouni, H. Cherifi

2006

Abstract

In this paper, we propose to use a class of filters based on fractional lower order statistics (FLOS) for still image restoration in the presence of α-stable noise. For this purpose, we present a family of 2-D finite-impulse response (FIR) adaptive filters optimized by the least mean lp-norm (LMP) algorithm. Experiments performed on natural images prove that the proposed algorithms provide superior performance in impulsive noise environments compared to LMS and Weighted Myriad filters.

References

  1. G. Aydin, O. Arikan, and E. Cetin. Robust adaptive filtering algorithms for a-stable random processes. IEEE Trans. On Circuits and Systems II, vol. 46, N. 2, pp. 198-202, February 1999.
  2. M. Shao, C. L. Nikias. Signal processing with fractionnal lower order moments : Stable processes and their applications. Proc. IEEE, vol. 81, pp. 986-1009, 1993.
  3. J. G. Gonzalez, G. R. Arce. Weighted myriad filters : A robust filtering framework derived from alphastable distributions. Proc. of the IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Atlanta, GA, May 1996, Vol. 5, pp. 2833-2836.
  4. J. G. Gonzales, G.R. Arce. Zero-order statistics : a signal processing framework for very impulsive processes. Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics, Banff, Canada, July 1997, pp. 254-258.
  5. P. Kidmose. Adaptive filtering for non-Gaussian noise processes. Proceedings of International Conference on Acoustics, Speech and Signal Processing, ICASSP2000, pp. 424-427.
  6. Ercan E. Kuruoglu, Peter J. W. Rayner, William J. Fitzgerald. Least lp-norm impulsive noise cancellation with polynomial filters. Signal Processing. v. 69, pp. 1-14. 1998.
  7. A. Ben Hamza and H. Krim. Image denoising : A nonlinear robust statistical approach. IEEE Trans. on Signal Processing, Vol. 49, N0. 12, Decembre 2001.
  8. Ercan E. Kuruoglu. Nonlinear least lp-norm filters for nonlinear autoregressive a-stable processes. Digital Signal Processing 12, 119-142 (2002).
  9. O. Tanrikulu and A. G. Constantindes, ”Least-mean Kurtosis : A novel higher-order statistics based adaptive filtering algorithm”, Electronics Letters, vol. 30, no. 3, pp. 189-190, February 1994.
  10. C. Kotropoulos and I. Pitas, Nonlinear Model-Based Image/Video Processing and Analysis, J. Wiley, 2001.
  11. A. H. Money, J. F. Affleck-Graves, G.D.I. Barr, The linear regression model: L-p-norm estimation and the choice of p, Communications in Statist. Simulation Comput. 11 (1) (1982) 89-109.
  12. Zhou Wang, Alan C. Bovik, Hamid R. Sheikh and Eero P. Simoncelli. Image quality assessment: from error measurement to structural similarity. To appear in IEEE transactions on image processing, vol. 13, No. 1, January 2004.
Download


Paper Citation


in Harvard Style

El Hassouni M. and Cherifi H. (2006). A STATISTICAL BASED APPROACH FOR REMOVING HEAVY TAIL NOISE FROM IMAGES . In Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, ISBN 972-8865-40-6, pages 157-161. DOI: 10.5220/0001371901570161


in Bibtex Style

@conference{visapp06,
author={M. El Hassouni and H. Cherifi},
title={A STATISTICAL BASED APPROACH FOR REMOVING HEAVY TAIL NOISE FROM IMAGES},
booktitle={Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,},
year={2006},
pages={157-161},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001371901570161},
isbn={972-8865-40-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,
TI - A STATISTICAL BASED APPROACH FOR REMOVING HEAVY TAIL NOISE FROM IMAGES
SN - 972-8865-40-6
AU - El Hassouni M.
AU - Cherifi H.
PY - 2006
SP - 157
EP - 161
DO - 10.5220/0001371901570161