Abstract
A neuro-fuzzy network approach to impulse noise filtering for gray scale images is presented. The network is constructed by combining four neuro-fuzzy filters with a postprocessor. Each neuro-fuzzy filter is a first order Sugeno type fuzzy inference system with 4-inputs and 1-output. The proposed impulse noise filter consists of two modes of operation, namely, training and testing (filtering). As demonstrated by the experimental results, the proposed filter not only has the ability of noise attenuation but also possesses desirable capability of details preservation. It significantly outperforms other conventional filters.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Pratt, W.K.: Digital Image Processing. Wiley Interscience, New York (1978)
Yli-Harja, O., Astola, J., Neuvo, Y.: Analysis of the properties of median and weighted median filters using threshold logic and stack filter representation. IEEE Trans. Signal Process. 39(2), 395–410 (1991)
Ko, S.J., Lee, Y.H.: Center weighted median filters and their applications to image enhancement. IEEE Trans. Circuits Syst. 38(9), 984–993 (1991)
Shuqun, Z., Karim, M.A.: A new impulse detector for switching median filters. IEEE Signal Process. Lett. 9(11), 360–363 (2002)
Tao, C., Hong Ren, W.: Space variant median filters for the restoration of impulse noise corrupted images. IEEE Trans. Circuits Syst. II Analog Digital Signal Process. 2001. 48(8), 784–789
Abreu, E., et al.: A new efficient approach for the removal of impulse noise from highly corrupted images. IEEE Trans. Image Process. 5(6), 1012–1025 (1996)
Russo, F., Ramponi, G.: A fuzzy filter for images corrupted by impulse noise. Signal Processing Letters, IEEE 3(6), 168–170 (1996)
Yuksel, M.E., Basturk, A.: A simple generalized neuro-fuzzy operator for efficient removal of impulse noise from highly corrupted digital images. AEU—Int. J. Electron.Commun. 59(1), 1–7 (2005)
Li, Y., Chung, F.-L., Wang, S.: A robust neuro-fuzzy network approach to impulse noise filtering for color images. Appl. Soft Comput. 8(2), 872–884 (2008)
Jang, J.-S.R., Sun, C.-T.: Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence, p. 614. Prentice-Hall, Inc, Upper Saddle River (1997)
Masters, T.: Advanced Algorithms for Neural Networks. John Wiley & Sons, New York (1995)
Hines, J.W.: Fuzzy and Neural Approaches in Engineering, MATLAB Supplement. Adaptive and Learning Systems for Signal Processing, Communications and Control Series. In: Haykin, S. (ed.) New York, John Wiley and Sons (1997)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, Y., Luo, H., Sun, J. (2013). A New Impulse Noise Filtering Algorithm Based on a Neuro-Fuzzy Network. In: Hatzilygeroudis, I., Palade, V. (eds) Combinations of Intelligent Methods and Applications. Smart Innovation, Systems and Technologies, vol 23. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36651-2_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-36651-2_3
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36650-5
Online ISBN: 978-3-642-36651-2
eBook Packages: EngineeringEngineering (R0)