Advertisement

An image dependent stopping method for iterative denoising procedures

  • Smaïl Akkoul
  • Rachid Harba
  • Roger Lédée
Communication Brief

Abstract

Iterative methods are very successful for denoising images corrupted by random valued impulse noise. However, choosing the optimal number of iterations is a difficult issue. In this letter, a stopping method is proposed: the iterative denoising process is stopped when the number of cleaned pixels is minimal. It corresponds to the moment when the denoising process tends to modify noise-free pixels. It also corresponds with a high precision to the maximum of PSNR of the restored image. The originality of the method is that no a priori iteration number is to be chosen but the method results from image information. The proposed stopping strategy is therefore an efficient and image dependent method that can be easily implemented on real data.

Keywords

Impulse noise Image denoising Impulse noise removal Iterative denoising Optimal iteration number 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Akkoul, S., Lédée, R., Leconge, R., Léger, C., Harba, R., Pesnel, S., Lerondel, S., Lepape, A., & Vilcahuaman, L. (2008). Comparison of image restoration methods for bioluminescence imaging. ICISP 08, Cherbourg, France, LNCS 5099, pp. 163–172Google Scholar
  2. Akkoul S., Lédée R., Leconge R., Harba R. (2010) A new adaptive switching median filter. IEEE Signal Processing Letters 16(6): 587–590CrossRefGoogle Scholar
  3. Brownrigg D. (1984) The weighted median filter. Communications of the Association for Computing Machinery 27: 807–818CrossRefGoogle Scholar
  4. Chen T., Ma K. K., Chen L. H. (1999) Tri-state median filter for image denoising. IEEE Transactions on Image Processing 8: 1834–1838CrossRefGoogle Scholar
  5. Dong Y., Xu S. (2007) A new directional weighted median filter for removal of random-value impulse noise. IEEE Signal Processing Letters 14(3): 193–196CrossRefGoogle Scholar
  6. Gonzalez R. C., Woods R. E. (2002) Digital image processing. Prentice-Hall, Englewood Cliffs, NJGoogle Scholar
  7. Kang, C. C., & Wang, W. J. (2008) Modified switching median filter with one more noise detector for impulse noise removal. International Journal of Electronics and Communications. doi: 10.1016/j.aeue.2008.08.009.
  8. Ko S. J., Lee Y. H. (1991) Center weighted median filters and their applications to image enhancement. IEEE Transactions on Circuits System 38: 984–993CrossRefGoogle Scholar
  9. Ng P. -E., Ma K. -K. (2006) A switching median filter with boundary discriminative noise detection for extremely corrupted images. IEEE Transactions on Image Processing 15(6): 1506–1516CrossRefGoogle Scholar
  10. Pesnel S., Akkoul S., Lédée R., Leconge R., Pillon A., Kruczynski A., Harba R., Lerondel S., Le Pape A. (2011) Use of an image restoration process to improve spatial resolution in bioluminescence imaging. Molecular Imaging 10(6): 446–452Google Scholar
  11. Sun T., Neuvo Y. (1994) Detail preserving median based filters in image processing. Pattern Recognition Letters 15: 341–347CrossRefGoogle Scholar
  12. Wan Y., Chen Q., Kang Y. (2010) Robust impulse noise variance estimation based on image histogram. IEEE Signal Processing Letters 17(5): 485–488CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique, Energétique (PRISME)Université d’OrléansOrléans Cedex2France

Personalised recommendations