Abstract
A novel method of iterative fuzzy control-based filtering (IFCF) is proposed in this paper. The proposed method has outstanding characteristics of removing impulse noise and smoothing out Gaussian noise while preserving edges and image details effectively. This filtering approach is mainly based on the idea of not letting each point in the area of concern being uniformly fired by each of the basic fuzzy rules. The extended iterative fuzzy control-based filter (EIFCF) and the modified iterative fuzzy control-based filter (MIFCF) are presented in this paper too. EIFCF is mainly based on the idea that in each iteration the universe of discourse gets more shrunk and by shrinking the domains of the fuzzy linguistics, i.e., by compressing their membership function the number of fired fuzzy rules will be forced to keep unchanged in order to preserve the ability of the filter. MIFCF aims to enhance the property of the IFCF via increasing the iteration number without loosing edge information. Experiment results show that the proposed image filtering method based on iterative fuzzy control and its different modifications are very useful for image processing.
Supported in part by the fund of the Applied Fundamental Research of Chongqing Science and Technology Committee under Grant 2000-6968; Supported by the fund of University Science and Technology under Grant SWNU 2004006.
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References
Arakawa, K.: Digital signal processing based on fuzzy rules. In: Proceedings of the Fifth IFSA World Congress, pp. 1305–1308 (1994)
Mancuso, M., Poluzzi, R., Rizzotto, G.: A fuzzy filter for dynamic range reduction and contrast enhancement. In: Proceedings of FUZZ-IEEE 1994 3rd IEEE Int. Conf. Fuzzy Systems, pp. 264–267 (1994)
Mastin, G.A.: Image Processing. Adaptive filters for digital image noise smoothing: an evaluation, Computer Vision, Graphics 31, 103–121 (1985)
Pal, S.K.: Fuzzy sets in image processing and recognition. In: Proceedings of FUZZ-IEEE 1992 First IEEE Int. Conf. Fuzzy Systems, pp. 119–126 (1992)
Krishnapuram, R., Keller, M.: Fuzzy sets theoretic approach to computer vision: an overview. In: Proceedings of FUZZ-IEEE 1992 First IEEE Int. Conf. Fuzzy Systems, pp. 135–142 (1992)
Chen, B.T., Chen, Y., Hsu, W.: Image processing and understanding based on the fuzzy inference approach. In: Proceedings of FUZZ-IEEE 1994 3rd IEEE Int. Conf. Fuzzy Systems, pp. 279–283 (1994)
Russo, F., Ramponi, G.: Edge detection by FIRE operators. In: Proceedings of FUZZ-IEEE 1994 3rd IEEE Int. Conf. Fuzzy Systems, pp. 249–253 (1994)
Russo, F.: A user-friendly research tool for image processing with fuzzy rules. In: Proceedings of FUZZ-IEEE 1992 First IEEE Int. Conf. Fuzzy Systems, pp. 561–568 (1992)
Russo, F., Ramponi, G.: An image enhancement technique based on the FIRE operator. In: Proceedings of ICIP 1995 2nd IEEE Int. Conf. Image Processing, vol. 1, pp. 155–158 (1995)
Choi, Y., Krishnapuram, R.: A robust approach to image enhancement based on fuzzy logic. IEEE Trans. Image Processing 6(6), 808–825 (1997)
Yu, Y.-Q.: Fuzzy control technique and fuzzy electrical devices for home-use, pp. 102–106. Publishing House of Beijing Aeronautical and Space University, Beijing (2000)
Taguchi, A.: A design method of fuzzy weighted median fillers. In: Proceedings of ICIP 1996 3rd IEEE Int. Conf. Image Processing, vol. 1, pp. 423–426 (1996)
Muneyasu, M., Wada, Y., Hinamoto, T.: Edge-preserving smoothing by adaptive nonlinear filters based on fuzzy control laws. In: Proceedings of ICIC 1996 3rd IEEE Int. Conf. Image Processing, vol. 1, pp. 785–788 (1996)
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Lu, Rh., Yang, M., Qiu, Yh. (2005). A Novel Method of Image Filtering Based on Iterative Fuzzy Control. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548706_26
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DOI: https://doi.org/10.1007/11548706_26
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