Abstract
We propose a fuzzy logic based punctual kriging technique for enhancing images corrupted by Gaussian noise. Punctual kriging is used to generate kernel weights employing the semivariances in the neighborhood of a pixel and empirically determined global semi-variogram. Semivariance is a measure of the degree of spatial differences between samples (pixel values). Superiority of kriging over other methods for noise cancellation in 1-D signals has been established. A quantitative analysis of the kriging technique, for image enhancement as compared to the Wiener filter shows that kriging performs inferior to Wiener filtering for image enhancement. We have proposed a new fuzzy logic based method which substantially improves the performance of the kriging for image enhancement. Experimental results are presented to illustrate the improvement in the results and the effectiveness of the new technique.
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References
Clark I (1979) Practical Geostatistics. Applied Science, London
Costa JP, Pronzato L, Thierry E (2000) Nonlinear prediction by kriging, with application to noise cancellation. Signal Processing 80:553–566
Farbiz F, Menhaj MB (2000) A fuzzy logic control based approach for image filtering. In: Fuzzy Techniques in Image Processing Vol 52, Studies in Fuzziness and Soft Computing. Springer-Verlag, New York, pp. 194–221.
Krige D (1951) A Statistical approach to some mine valuation and allied problems on the Witwatersrand. Master Thesis, University of Witwatersrand
Leontaritis I, Billings S (1985) Input-output parametric models for nonlinear systems part 2: stochastic nonlinear systems. Int J Control 41(2):329–344
Naser El-Sheimy (1999) Digital Terrain Modeling, ENGO 573. The University of Calgary, Canada.
Pham TD and Wagner M (2000) Image Enhancement by Kriging and Fuzzy Sets. Int J Pattern Rec and Artificial Intelligence 14(8):1025–1038
Rugh WJ (1981) Nonlinear System Theory: The Volterra/Wiener Approach. The Johns Hopkins University Press, Baltimore
Tizhoosh H (2000) Fuzzy Image Enhancement: An Overview. In: Fuzzy Techniques in Image Processing, Studies in Fuzziness and Soft Computing, vol 52. Springer-Verlag, New York, pp. 137–171.
Wang Z, Bovik AC, Shiekh HR, Simoncelli EP (2004) Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Trans on Image Processing 13(4):600–612
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© 2005 Springer-Verlag Berlin Heidelberg
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Mirza, A.M., Munir, B. (2005). Combining Fuzzy Logic and Kriging for Image Enhancement. In: Reusch, B. (eds) Computational Intelligence, Theory and Applications. Advances in Soft Computing, vol 33. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31182-3_42
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DOI: https://doi.org/10.1007/3-540-31182-3_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22807-3
Online ISBN: 978-3-540-31182-9
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