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Dual Sliding Statistics Switching Median Filter for the Removal of Low Level Random-Valued Impulse Noise

  • Suid, Mohd Helmi (Faculty of Electrical and Electronic Engineering, Universiti Malaysia Pahang) ;
  • Jusof, M F.M. (Faculty of Electrical and Electronic Engineering, Universiti Malaysia Pahang) ;
  • Ahmad, Mohd Ashraf (Faculty of Electrical and Electronic Engineering, Universiti Malaysia Pahang)
  • Received : 2016.04.28
  • Accepted : 2018.01.22
  • Published : 2018.05.01

Abstract

A new nonlinear filtering algorithm for effectively denoising images corrupted by the random-valued impulse noise, called dual sliding statistics switching median (DSSSM) filter is presented in this paper. The proposed DSSSM filter is made up of two subunits; i.e. Impulse noise detection and noise filtering. Initially, the impulse noise detection stage of DSSSM algorithm begins by processing the statistics of a localized detection window in sorted order and non-sorted order, simultaneously. Next, the median of absolute difference (MAD) obtained from both sorted statistics and non-sorted statistics will be further processed in order to classify any possible noise pixels. Subsequently, the filtering stage will replace the detected noise pixels with the estimated median value of the surrounding pixels. In addition, fuzzy based local information is used in the filtering stage to help the filter preserves the edges and details. Extensive simulations results conducted on gray scale images indicate that the DSSSM filter performs significantly better than a number of well-known impulse noise filters existing in literature in terms of noise suppression and detail preservation; with as much as 30% impulse noise corruption rate. Finally, this DSSSM filter is algorithmically simple and suitable to be implemented for electronic imaging products.

Acknowledgement

Supported by : Universiti Malaysia Pahang

References

  1. W. Luo., "Efficient removal of impulse noise from digital images," IEEE Trans. Consumer Electronics, vol. 52, no. 2, pp. 523-527, 2006. https://doi.org/10.1109/TCE.2006.1649674
  2. R. C. Gonzalez and R. E. Woods., "Digital image processing," New York: Addison-Wesley, 1992.
  3. H. Hwang and R. A. Haddad., "Adaptive Median Filters: New Algorithms and Results," IEEE Transactions on Image Processing, vol. 4, no. 4, pp. 499-502, 1995. https://doi.org/10.1109/83.370679
  4. T. Sun and Y. Neuvo., "Detail-Preserving Median Based Filters in Image Processing," Pattern Recognition Letters, 15 (1994) pp. 341-347. https://doi.org/10.1016/0167-8655(94)90082-5
  5. T. Chen and H. R. Wu., "Space variant median filters for the restoration of impulse noise corrupted images," IEEE Trans. on Circuits and Systems II, 48, pp. 784-789, 2001. https://doi.org/10.1109/82.959870
  6. S. Zhang and M. A. Karim., "A new impulse detector for switching median filters," IEEE Signal Processing Letters, vol. 9, no. 11, pp. 360-363, 2002. https://doi.org/10.1109/LSP.2002.805310
  7. I. Aizenberg and C. Butakoff., "Effective impulse detector based on rank-order criteria," IEEE Signal Process. Letters, vol. 3, no. 11, pp. 363-366, 2004.
  8. Y. Dong and S. Xu., "A new directional weighted median filter for removal of random-valued impulse noise," IEEE Signal Processing Letters, vol. 14, no. 3, pp. 193-196, 2007. https://doi.org/10.1109/LSP.2006.884014
  9. H. H. Chou and L. Y Hsu., "A noise-ranking switching filter for images with general fixed impulse noises," Signal Processing, 6, pp. 198-208, 2015.
  10. T. Chen and H. R. Wu., "Adaptive impulse detection using centre-weighted median filter," IEEE Signal Processing Letters, vol. 8, no. 1, pp. 1-3 2001. https://doi.org/10.1109/97.889633
  11. Chen, K. K. Ma and L. H. Chen., "Tri-state median filter for image denoising," IEEE Transactions on Image Processing, vol. 8, no. 12, pp. 1834-1838, 1998.
  12. J. K. Sung, H. L. Yong., "Center weighted median filters and their applications to image enhancement," IEEE Transactions on Circuits and Systems, vol. 38, no. 9, pp. 984-983, 1991. https://doi.org/10.1109/31.83870
  13. W. Luo., "An efficient algorithm for the removal of impulse noise from corrupted images," AEU-Int. J. Electron. Commun., 61, pp. 551-555, 2007. https://doi.org/10.1016/j.aeue.2006.10.002
  14. K. K. V. Toh and N. A. Mat Isa., "Cluster-based adaptive fuzzy switching median filter for universal impulse noise reduction," IEEE Trans. Consumer Electronics, vol. 56, no. 4, pp. 2560-2568, 2010. https://doi.org/10.1109/TCE.2010.5681141
  15. http://homepages.inf.ed.ac.uk/rbf/HIPR2/median.htm (accessed on April 2012).
  16. Majid, A., Lee, CH., Mahmood, M.T. et al. "Impulse noise filtering based on noise-free pixels using genetic programming," Knowl Inf Syst (2012) 32: p. 505. https://doi.org/10.1007/s10115-011-0456-7
  17. Xu, Z., Wu, H.R., Yu, X. et al. "Adaptive progressive filter to remove impulse noise in highly corrupted color images," SIViP (2013) 7: p. 817. https://doi.org/10.1007/s11760-011-0271-3
  18. Ilke Turkmen., "A new method to remove randomvalued impulse noise in images," Int. J. Electron. Commun. (AEU) 67 (2013) 771-779. https://doi.org/10.1016/j.aeue.2013.03.006
  19. M. S. Darus, S. N. Sulaiman, I. S. Isa et al. "Modified hybrid median filter for removal of low density random-valued impulse noise in images," IEEE International Conference on Control System, Computing and Engineering, pp. 25-27 November 2016.
  20. M. H Suid and N. A. M. Isa. Scientific Research and Essays, vol. 7, no. 7, pp. 813-823, 2012.