Summary
In this chapter, seven fuzzy filters for noise reduction in images are introduced. These seven fuzzy filters include the Gaussian fuzzy filter with median center (GMED), the symmetrical triangular fuzzy filter with median center (TMED), the asymmetrical triangular fuzzy filter with median center (ATMED), the Gaussian fuzzy filter with moving average center (GMAV), the symmetrical triangular fuzzy filter with moving average center (TMAV), the asymmetrical triangular fuzzy filter with moving average center (ATMAV), and the decreasing weight fuzzy filter with moving average center (DWMAV). Each of these fuzzy filters, applies a weighted membership function to an image within a window to determine the center pixel, is easy and fast to implement. Simulation results on the filtering performance of these seven fuzzy filters and the standard median filter (MED) and moving average filter (MAV) on images contaminated with low, medium, high impulse and random noises are presented. Results indicate that these seven fuzzy filters achieve varying successes in noise reduction in images as compared to the standard MED and MAV filters.
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References
I. Pitas and A. N. Venetsanopoulos, Nonlinear digital filters, Kluwer Academic Publishers, 1990.
S. Agaian, J. Astola, and K. Egiazarian, Binary polynomial transformations and nonlinear digital filters, Marcel Dekker, Inc., 1995.
S. K. Mitra and G. Sicuranza, Eds., Nonlinear Image Processing, Academic Press, 2000.
E. E. Kerre and M. Nachtegael, Eds., Fuzzy techniques in image processing, Series on Studies in Fuzziness and Soft Computing, Vol. 52, Springer-Verlag, 2000.
F. Russo, Recent advances in fuzzy techniques for image enhancement, IEEE Transactions on Instrumentation and Measurement, vol. 47, no. 6, pp. 14281434, Dec. 1998.
M. Nachtegael, D. Van der Weken, A. Van De Ville, E. Kerre, W. Philips, I. Lemahieu, An overview of classical and fuzzy-classical filters, Proceedings of IEEE International Conference on Fuzzy Systems, pp. 3–6, 2001.
M. Nachtegael, D. Van der Weken, A. Van De Ville, E. Kerre, W. Philips, I. Lemahieu, An overview of fuzzy filters for noise reduction, Proceedings of IEEE International Conference on Fuzzy Systems, pp. 7–10, 2001.
M. Nachtegael, D. Van der Weken, A. Van De Ville, E. Kerre, W. Philips, I. Lemahieu, A comparative study of classical and fuzzy filters for noise reduction, Proceedings of IEEE International Conference on Fuzzy Systems, pp. 11–14, 2001.
J. S. Lim, Two-dimensional signal and image processing, Prentice-Hall, pp. 536–540, 1990.
K. Arajawa, Median filter based on fuzzy rules and its application to image restoration, Fuzzy Sets and Systems, Vol. 77, pp. 3–, 1996.
K. Arajawa, Fuzzy ruled-based image processing with optimization, in Fuzzy Techniques in Image Processing, Edited by E. E. Kerre and M. Nachtegael, Springer-Verlag, pp. 222–247, 2000.
C.-S. Lee, Y.-H. Kuo, and P.-T. Yu, Weighted fuzzy mean filters for image processing, Fuzzy Sets and Systems, Vol. 89, pp. 157–180, 1997.
C.-S. Lee, Y.-H. Kuo, Adaptive fuzzy filter and its application to image processing, in Fuzzy Techniques in Image Processing, Edited by E. E. Kerre and M. Nachtegael, Springer-Verlag, pp. 172–193, 2000.
F. Russo and G. Ramponi, A fuzzy filter for images corrupted by impulse noise, IEEE Signal Processing Letters, Vol. 3, No. 6, pp. 168–170, June 1996.
F. Russo and G. Ramponi, Removal of impulse noise using a fire filter, Proceedings of IEEE International Conference in Image Processing, pp. 975–978, 1996.
F. Russo, FIRE operators for image processing, Fuzzy Sets and Systems, Vol. 103, pp. 265–275, 1999.
F. Russo, Noise cancellation using nonlinear fuzzy filters, Proceedings of IEEE Instrumentation and Measurement Technology Conference, Ottawa, Canada, pp. 772–777, May 1997.
F. Farbiz and M. B. Menhaj, A fuzzy logic control based approach for image filtering, in Fuzzy Techniques in Image Processing, edited by E. E. Kerre and M. Nachtegael, Springer-Verlag, pp. 194–221, 2000.
D. Van De Vile, M. Nachtegael, D. Van der Weken, W. Philips, I. Lemahieu, E. E. Kerre, A new fuzzy filter for Gaussian noise reduction, Proceedings of International SPIE Conference on Electronic Imaging, pp. 1–9, 2001.
A. Taguchi, H. Takashima, and Y. Murata, Fuzzy filters for image smoothing, in Proceedings of SPIE Conference on Nonlinear Image Processing V, San Jose, CA, pp. 332–339, Feb. 1994.
A. Taguchi, H. Takashima, and F. Russo, Data dependent filtering using the fuzzy inference, in Proceedings of IEEE Instrumentation Measurement Technology ConferenceM, Waltham, MA, pp. 752–756, April 1995.
H. K. Kwan and Y. Cai, Median filtering using fuzzy concept, Proceedings of 36th Midwest Symposium on Circuits and Systems, Detroit, Michigan, USA, vol. 2, August 15–18, 1993, pp. 824–827.
H. K. Kwan and Y. Cai, Fuzzy filters for image filtering, Proceedings of 45th Midwest Symposium on Circuits and Systems, Oklahoma, August 25–28, 2002.
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Kwan, H.K. (2003). Fuzzy Filters for Noise Reduction in Images. In: Nachtegael, M., Van der Weken, D., Kerre, E.E., Van De Ville, D. (eds) Fuzzy Filters for Image Processing. Studies in Fuzziness and Soft Computing, vol 122. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36420-7_2
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DOI: https://doi.org/10.1007/978-3-540-36420-7_2
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