Skip to main content
SpringerLink
Log in
Book cover

International Conference on Advances in Information Systems

ADVIS 2004: Advances in Information Systems pp 383–391Cite as

Using an Exact Radial Basis Function Artificial Neural Network for Impulsive Noise Suppression from Highly Distorted Image Databases

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3261))

Abstract

In this paper, a new filter, RM, which is based on exact radial basis function artificial neural networks, is proposed for the impulsive noise suppression from highly distorted images. The RM uses Chi-Squared based goodness-of-fit test in order to find corrupted pixels more accurately.The proposed filter shows a high performance at the restoration of images distorted by impulsive noise. The extensive simulation results show that the proposed filter achieves a superior performance to the other filters mentioned in this paper in the cases of being effective in noise suppression and detail preservation, especially when the noise density is very high.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Breveglieri, L., Piuri, V.: Digital median filters. Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology 31, 191–206 (2002)

    MATH  Google Scholar 

  2. Nodes, T.A., Gallagher, N.C.: Median filters: some modifications and their properties. IEEE Trans. Acoust., Speech, Signal Processing 30(5), 739–746 (1982)

    Article  Google Scholar 

  3. Sun, T., Neuvo, Y.: Detail-preserving median based filters in image processing. Pattern Recognit. Lett. 15, 341–347 (1994)

    Article  Google Scholar 

  4. Lin, H., Willson, A.N.: Median filter with adaptive length. IEEE Trans. Circuits Syst. 35, 675–690 (1988)

    Article  MathSciNet  Google Scholar 

  5. Tukey, J.W.: Nonlinear (nonsuperposable) methods for smoothing data. In: Cong. Rec. EASCON 1974, p. 673 (1974)

    Google Scholar 

  6. Wang, Z., Zhang, D.: Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE Trans. on Circuits and Systems-II: Analog and Digital Signal Processing 46(1), 78–80 (1999)

    Article  Google Scholar 

  7. Abreu, E., et al.: A new efficient approach for the removal of impulse noise from highly corrupted images. IEEE Trans. Image Processing 5(6), 1012–1025 (1996)

    Article  MathSciNet  Google Scholar 

  8. Chen, T., Wu, H.R.: A new class of median based impulse rejecting filters. IEEE International Conference on Image Processing 1, 916–919 (2000)

    Google Scholar 

  9. Chen, T., et al.: Adaptive impulse detection using center weighted median filters. IEEE Signal Processing Letters 8(1), 1–3 (2001)

    Article  Google Scholar 

  10. Ko, S.J., Lee, Y.H.: Center weighted median filters and their applications to image enhancement. IEEE Trans. Circuits Systems II 43(3), 157–192 (1996)

    Article  Google Scholar 

  11. Russo, F., Ramponi, G.: A Fuzzy Filter for Images Corrupted By Impulse Noise. IEEE Signal Processing Letters 6(3), 168–170 (1996)

    Article  Google Scholar 

  12. Russo, F.: Evolutionary neural fuzzy systems for data filtering. In: IEEE Instrumentation and Measurement Technology Conference, vol. 2, pp. 826–830 (1998)

    Google Scholar 

  13. Potamitis, Fakotakis, N.D., Kokkinakis, G.: Impulsive noise suppression using neural networks. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 3, pp. 1871–1874 (2000)

    Google Scholar 

  14. Acosta, F.M.A.: Radial basis function and related models: An overview. In: Signal Processing, vol. 45, pp. 37–58. Elsevier Science Publishers, Amsterdam (1995)

    Google Scholar 

  15. Billings, S.A., Fung, C.F.: Recurrent radial basis function networks for adaptive noise cancellation. Neural Networks 8(2), 273–290 (1995)

    Article  Google Scholar 

  16. Cha, I., Kassam, S.A.: Channel equalization using adaptive complex radial basis function networks. IEEE Journal on Selected Areas in Communications 13(1), 122–131 (1995)

    Article  Google Scholar 

  17. Cha, Kassam, S.A.: Interference cancellation using radial basis function networks. Signal Processing 47, 247–268 (1995)

    Article  MATH  Google Scholar 

  18. Chen, S.: Nonlinear time series modelling and prediction using Gaussian RBF networks with enhanced clustering and RLS learning. Electronics Letters 31(2), 117–118 (1995)

    Article  Google Scholar 

  19. Chen, S., Mulgrew, B., Grant, P.M.: A clustering technique for digital communications channel equalization using radial basis function networks. IEEE Transactions on Neural Networks 4(4), 570–579 (1993)

    Article  Google Scholar 

  20. Haykin, S.: Neural networks. Macmillan, New York (1994)

    MATH  Google Scholar 

  21. MathWorks, MATLAB the language of technical computing, MATLAB Function Reference. The MathWorks, Inc., New York (2002)

    Google Scholar 

  22. Yang, T.Y.: Finite element structural analysis, pp. 446–449. Prentice Hall, Englewood Cliffs (1986)

    Google Scholar 

  23. Watson, D.F. (ed.): Contouring: A guide to the analysis and display of spacial data, pp. 101–161. Pergamon, New York (1994)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Civil Aviation School, Avionics Dept., Erciyes University, Kayseri, Turkey

    Pınar Çivicioğlu

  2. Engineering Faculty, Electronic Engineering Dept., Erciyes University, Kayseri, Turkey

    Mustafa Alçı

  3. Engineering Faculty, Geodesy and Photogrammetry Engineering Dept., Erciyes University, Kayseri, Turkey

    Erkan Beṣdok

Authors
  1. Pınar Çivicioğlu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mustafa Alçı
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Erkan Beṣdok
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dokuz Eylül University, lzmir, Turkey

    Tatyana Yakhno

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Çivicioğlu, P., Alçı, M., Beṣdok, E. (2004). Using an Exact Radial Basis Function Artificial Neural Network for Impulsive Noise Suppression from Highly Distorted Image Databases. In: Yakhno, T. (eds) Advances in Information Systems. ADVIS 2004. Lecture Notes in Computer Science, vol 3261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30198-1_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30198-1_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23478-4

  • Online ISBN: 978-3-540-30198-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation