Using an exact radial basis function artificial neural network for lmpulsive noise suppression from highly distorted image databases

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3261, Issue , 2004, Pages 383-391

  Çivicioǧlu, Pmar ^a   Alçi, Mustafa ^a   Besdok, Erkan ^a  

^a ERCIYES UNIVERSITY   (Turkey)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE SEGMENTATION; RADIAL BASIS FUNCTION NETWORKS; FUNCTIONS; INFORMATION SYSTEMS; NEURAL NETWORKS; SPURIOUS SIGNAL NOISE;

CHI-SQUARED; DISTORTED IMAGES; EXTENSIVE SIMULATIONS; GOODNESS OF FIT; IMAGE DATABASE; IMPULSIVE NOISE SUPPRESSION; NOISE SUPPRESSION; PERFORMANCE; RADIAL BASIS FUNCTION ARTIFICIAL NEURAL NETWORKS; RESTORATION OF IMAGES; DETAIL PRESERVATION; GOODNESS-OF-FIT TEST;

IMPULSE NOISE;

EID: 24944566880     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Article

References (23)

1. L. Breveglieri, V. Piuri, Digital median filters, Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology, 31, (2002), 191-206.
2. T.A. Nodes, N.C. Gallagher, Median filters: some modifications and their properties, IEEE Trans. Acoust., Speech, Signal Processing, 30 (5), (1982), 739-746.
3. T. Sun, Y. Neuvo, Detail-preserving median based filters in image processing, Pattern Recognit. Lett., 15, (1994), 341-347.
4. H. Lin and A. N. Willson, Median filter with adaptive length, IEEE Trans. Circuits Syst., 35, (1988), 675-690.
5. J.W. Tukey, Nonlinear (nonsuperposable) methods for smoothing data, Cong. Rec. EASCON'74, (1974), 673.
6. Z. Wang, D. Zhang, 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), (1999), 78-80.
7. E. Abreu, et.al., A new efficient approach for the removal of impulse noise from highly corrupted images, IEEE Trans. Image Processing, 5 (6), (1996), 1012-1025.
8. T. Chen, H.R. Wu, A new class of median based impulse rejecting filters, IEEE International Conference on Image Processing, 1, (2000), 916-919.
9. T. Chen., et.al., Adaptive impulse detection using center weighted median filters, IEEE Signal Processing Letters, 8 (1), (2001), 1-3.
10. S.J. Ko, Y. H. Lee, Center weighted median filters and their applications to image enhancement, IEEE Trans. Circuits Systems II, 43 (3), (1996), 157-192.
11. F. Russo, G. Ramponi, A Fuzzy Filter for Images Corrupted By Impulse Noise, IEEE Signal Processing Letters, 6 (3), (1996), 168-170.
12. F. Russo, Evolutionary neural fuzzy systems for data filtering, IEEE Instrumentation and Measurement Technology Conference, 2, (1998), 826-830.
13. I. Potamitis, N.D. Fakotakis, G. Kokkinakis, Impulsive noise suppression using neural networks, IEEE International Conference on Acoustics, Speech, and Signal Processing, 3, (2000), 1871-1874.
14. F.M.A. Acosta, Radial basis function and related models: An overview, Signal Processing, Elsevier Science Publishers, 45, (1995), 37-58.
15. S.A. Billings and C.F. Fung, Recurrent radial basis function networks for adaptive noise cancellation, Neural Networks, Elsevier Science Publishers, 8 (2), (1995), 273-290.
16. I. Cha and S.A. Kassam, Channel equalization using adaptive complex radial basis function networks, IEEE Journal on Selected Areas in Communications, 13 (1), (1995), 122-131.
17. I. Cha and S.A. Kassam, Interference cancellation using radial basis function networks, Signal Processing, Elsevier Science Publishers, 47, (1995), 247-268.
18. S. Chen, Nonlinear time series modelling and prediction using Gaussian RBF networks with enhanced clustering and RLS learning, Electronics Letters, 31 (2), (1995), 117-118.
19. S. Chen, B. Mulgrew, and P.M. Grant, A clustering technique for digital communications channel equalization using radial basis function networks, IEEE Transactions on Neural Networks, 4 (4), (1993), 570-579.
20. S. Haykin, Neural networks, Macmillan, New York, (1994).
21. MathWorks, MATLAB the language of technical computing, MATLAB Function Reference. New York: The MathWorks, Inc., (2002).
22. T.Y. Yang, Finite element structural analysis, Prentice Hall, Englewood Cliffs, NJ, (1986), 446-449.
23. D.F. Watson, Contouring: A guide to the analysis and display of spacial data, Pergamon, New York, (1994), 101-161.