Salt and Pepper Noise Reduction Schemes Using Cellular Automata

  • Deepak Ranjan Nayak
  • Ratnakar Dash
  • Banshidhar Majhi
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 44)


Two filters in the light of two-dimensional Cellular Automata (CA) are presented in this paper for salt and pepper noise reduction of an image. The design of a parallel algorithm to remove noise from corrupted images is a demanded approach now, so we utilize the idea of cellular automata to cater this need. The filters are mainly designed according to the neighborhood structure of a cell with different boundary conditions. The performances of the proposed filters with that of existing filters are evaluated in terms of peak signal-to-noise ratio (PSNR) values and it has been observed that the proposed filters are extremely promising for noise reduction of an image contaminated by salt and pepper noise. The primary point of interest in utilizing these proposed filters is; it preserves more image details in expense of noise suppression.


Cellular automata (CA) Boundary condition Impulse noise Peak signal-to-noise ratio NNBCA TFNBCA 


  1. 1.
    Selvapeter, P.J., Hordijk, W.: Cellular automata for image noise filtering. IEEE Conf. (NaBIC), 193–197 (2009)Google Scholar
  2. 2.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn edn. Prentice-Hall, New Delhi (2002)Google Scholar
  3. 3.
    Yin, L., Yang, R., Gabbouj, M., Neuvo, Y.: Weighted median filters: a tutorial. IEEE Trans. Circ. Syst. II: Anal Digit Signal Process. 43, 157–92 (1996)Google Scholar
  4. 4.
    Ko, S.J., Lee, Y.H.: Center weighted median filters and their applications to image enhancement. IEEE Trans. Circ. Syst. 38, 984–993 (1991)CrossRefGoogle Scholar
  5. 5.
    Akkoul, S., Ledee, R., Leconge, R., Harba, R.: A new adaptive switching median filter. IEEE Signal Process. Lett. 17, 587–590 (2010)CrossRefGoogle Scholar
  6. 6.
    Wang, Z., Zhang, D.: Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE Trans Circ. Syst II: Anal Dig Signal Process. 46, 78–80 (1999)CrossRefGoogle Scholar
  7. 7.
    Chen, T., Wu, H.R.: Adaptive impulse detection using center-weighted median filters. IEEE Signal Process. Lett. 8, 1–3 (2001)CrossRefGoogle Scholar
  8. 8.
    Srivasan, K.S., Ebenezer, D.:A new fast and efficient decision-based algorithm for removal of high-density impulse noises. IEEE Signal Process. Lett. 14(3) (2007)Google Scholar
  9. 9.
    Toh, K., Isa, N.: Noise adaptive fuzzy switching median filter for salt-and-pepper noise reduction. Signal Process Lett. IEEE. 17, 281–284 (2010)CrossRefGoogle Scholar
  10. 10.
    Sadeghi, S., Rezvanian, A., Kamrani, E.: An efficient method for impulse noise reduction from images using fuzzy cellular automata. International J. Elec. Comm. 772–779 (2012)Google Scholar
  11. 11.
    Rosin, P.L.: Training cellular automata for image processing. IEEE Trans. Image Process. 15(7), 2076–2087 (2006)CrossRefGoogle Scholar
  12. 12.
    Wolfram, S.:Computation theory of cellular automata. Commun. Math. Phys., 14–57 (1984)Google Scholar
  13. 13.
    Chang, C., Zhang, Y., Gdong, Y. :Cellular automata for edge detection of images. IEEE Proc. Mach. Learn. Cybern. 26–29 (2004)Google Scholar
  14. 14.
    Sahoo, S., Choudhury, P.P., Pal, A., Nayak, B.K.: Solutions on 1-D and 2-D density classification problem using programmable cellular automata. J. Cell. Automata 9(1), 59–88 (2014)Google Scholar
  15. 15.
    Nayak, D.R., Patra, P.K., Mahapatra, A.: A survey on two dimensional cellular automata and its application in image processing. arXiv: 1407.7626 [cs.CV] (2014)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  • Deepak Ranjan Nayak
    • 1
  • Ratnakar Dash
    • 1
  • Banshidhar Majhi
    • 1
  1. 1.Department of Computer Science and EngineeringNational Institute of TechonoloyRourkelaIndia

Personalised recommendations