A new fuzzy filter is presented for noise reduction of
images corrupted with additive noise. The filter consists of two
stages. In the first stage, all the pixels of image are processed for
determining noisy pixels. For this, a fuzzy rule based system
associates a degree to each pixel. The degree of a pixel is a real
number in the range [0,1], which denotes a probability that the pixel
is not considered as a noisy pixel. In the second stage, another fuzzy
rule based system is employed. It uses the output of the previous
fuzzy system to perform fuzzy smoothing by weighting the
contributions of neighboring pixel values. Experimental results are
obtained to show the feasibility of the proposed filter. These results
are also compared to other filters by numerical measure and visual
inspection.
[1] K. Arakawa, Fuzzy rule-based image processing with optimization,
Fuzzy techniques in image processing, Springer-Verlag, 2000.
[2] D. Van De Ville, M. Nachtegael, D. Van der Weken, E. Kerre, W.
Philips, and I. Lemahieu, Noise Reduction by Fuzzy Image Filtering,
IEEE transactions on fuzzy systems, vol. 11, No. 4, August 2003.
[3] F.Farbiz, M. Menhaj, S. Motamedi, Fixed Point Filter Design for Image
Enhancement using Fuzzy Logic, In Proc. IEEE, 1998.
[4] P. Liu and H. Li, Fuzzy techniques in image restoration research- A
survey, International Journal of Computational Cognition, Volume 2, pp
131-149, June 2004.
[5] F. Russo and G. Ramponi, A fuzzy operator for the enhancement of
blurred and noisy images, IEEE Trans. Image Processing, vol. 4,
pp.1169-1174, August 1995.
[6] F. Russo, "Fire operators for image processing," Fuzzy Sets Syst., vol.
103, no. 2, pp. 265-275, 1999.
[7] C.-S. Lee, Y.-H. Kuo, and P.-T. Yu, Weighted fuzzy mean filters for
image processing, Fuzzy Sets Syst., no. 89, pp. 157-180, 1997.
[8] C. Lee and Y. Kuo, Fuzzy Techniques in Image Processing. New York:
Springer-Verlag, 2000, vol. 52, Studies in Fuzziness and Soft
Computing, ch. Adaptive fuzzy filter and its application to image
enhancement, pp. 172-193.
[9] F. Farbiz and M. B. Menhaj, Fuzzy Techniques in Image Processing.
New York: Springer-Verlag, 2000, vol. 52, Studies in Fuzziness and
Soft Computing, ch. A fuzzy logic control based approach for image
filtering, pp. 194-221.
[10] K. Arakawa, Median filter based on fuzzy rules and its application to
image restoration, Fuzzy Sets Syst., pp. 3-13, 1996.
[11] Y.Choi and R. Krishnapuram, A robust approach to image enhancement
based on fuzzy logic, IEEE Trans. Image Processing, vol. 6, no. 6, June
1997, p.808-825.
[12] A. Taguchi, A design method of fuzzy weighted median filters, In Proc.
Third IEEE Int. Conf Image Processing, vol. 1,.1996 p.423-426.
[13] M. Muneyasu, Y. Wada, T. Hinamoto, Edge-preserving smoothing by
adaptive nonlinear filters based on fuzzy control laws, In Proc. Third
IEEE Int. Conf Image Processing,Vol.1,1996. pp. 785-788.
[1] K. Arakawa, Fuzzy rule-based image processing with optimization,
Fuzzy techniques in image processing, Springer-Verlag, 2000.
[2] D. Van De Ville, M. Nachtegael, D. Van der Weken, E. Kerre, W.
Philips, and I. Lemahieu, Noise Reduction by Fuzzy Image Filtering,
IEEE transactions on fuzzy systems, vol. 11, No. 4, August 2003.
[3] F.Farbiz, M. Menhaj, S. Motamedi, Fixed Point Filter Design for Image
Enhancement using Fuzzy Logic, In Proc. IEEE, 1998.
[4] P. Liu and H. Li, Fuzzy techniques in image restoration research- A
survey, International Journal of Computational Cognition, Volume 2, pp
131-149, June 2004.
[5] F. Russo and G. Ramponi, A fuzzy operator for the enhancement of
blurred and noisy images, IEEE Trans. Image Processing, vol. 4,
pp.1169-1174, August 1995.
[6] F. Russo, "Fire operators for image processing," Fuzzy Sets Syst., vol.
103, no. 2, pp. 265-275, 1999.
[7] C.-S. Lee, Y.-H. Kuo, and P.-T. Yu, Weighted fuzzy mean filters for
image processing, Fuzzy Sets Syst., no. 89, pp. 157-180, 1997.
[8] C. Lee and Y. Kuo, Fuzzy Techniques in Image Processing. New York:
Springer-Verlag, 2000, vol. 52, Studies in Fuzziness and Soft
Computing, ch. Adaptive fuzzy filter and its application to image
enhancement, pp. 172-193.
[9] F. Farbiz and M. B. Menhaj, Fuzzy Techniques in Image Processing.
New York: Springer-Verlag, 2000, vol. 52, Studies in Fuzziness and
Soft Computing, ch. A fuzzy logic control based approach for image
filtering, pp. 194-221.
[10] K. Arakawa, Median filter based on fuzzy rules and its application to
image restoration, Fuzzy Sets Syst., pp. 3-13, 1996.
[11] Y.Choi and R. Krishnapuram, A robust approach to image enhancement
based on fuzzy logic, IEEE Trans. Image Processing, vol. 6, no. 6, June
1997, p.808-825.
[12] A. Taguchi, A design method of fuzzy weighted median filters, In Proc.
Third IEEE Int. Conf Image Processing, vol. 1,.1996 p.423-426.
[13] M. Muneyasu, Y. Wada, T. Hinamoto, Edge-preserving smoothing by
adaptive nonlinear filters based on fuzzy control laws, In Proc. Third
IEEE Int. Conf Image Processing,Vol.1,1996. pp. 785-788.
@article{"International Journal of Electrical, Electronic and Communication Sciences:55731", author = "Hamed Vahdat Nejad and Hameed Reza Pourreza and Hasan Ebrahimi", title = "A Novel Fuzzy Technique for Image Noise Reduction", abstract = "A new fuzzy filter is presented for noise reduction of
images corrupted with additive noise. The filter consists of two
stages. In the first stage, all the pixels of image are processed for
determining noisy pixels. For this, a fuzzy rule based system
associates a degree to each pixel. The degree of a pixel is a real
number in the range [0,1], which denotes a probability that the pixel
is not considered as a noisy pixel. In the second stage, another fuzzy
rule based system is employed. It uses the output of the previous
fuzzy system to perform fuzzy smoothing by weighting the
contributions of neighboring pixel values. Experimental results are
obtained to show the feasibility of the proposed filter. These results
are also compared to other filters by numerical measure and visual
inspection.", keywords = "Additive noise, Fuzzy logic, Image processing,
Noise reduction.", volume = "2", number = "9", pages = "1917-6", }