Abstract
In this paper, a fuzzy based impulse noise removal technique has been proposed. The proposed filter is based on noise detection, fuzzy set construction, histogram estimation and fuzzy filtering process. Noise detection process is used to identify the set of noisy pixels which are used for estimating the histogram of the original image. Estimated histogram of the original image is used for fuzzy set construction using fuzzy number construction algorithm. Fuzzy filtering process is the main component of the proposed technique. It consists of fuzzification, defuzzification and predicted intensity processes to remove impulse noise. Sensitivity analysis of the proposed technique has been performed by varying the number of fuzzy sets. Experimental results demonstrate that the proposed technique achieves much better performance than state-of-the-art filters. The comparison of the results is based on global error measure as well as local error measures i.e. mean square error (MSE) and structural similarity index measure (SSIM).
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References
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Pearson education Inc., London (2002)
Mirza, A.M., Chaudhry, A., Munir, B.: Spatially adaptive image restoration using fuzzy punctual kriging. Journal of Computer Science and Technology 22(4), 580–589 (2007)
Liu, P., Li, H.: Fuzzy Techniques in Image Restoration Research - a Survey (invited paper). International Journal of Computational Cognition 2(2), 131–149 (2004)
Tukey, J.W.: Exploratory Data Analysis. Addison-Wesley, Reading (1971)
Astola, J., Kuosmanen, P.: Fundamentals of Nonlinear Digital Filtering. CRC, Boca Raton (1997)
Pitas, I., Venetsanopoulos, A.: Nonlinear Digital Filters: Principles and Application. Kluwer, Norwell (1990)
Wang, J.H., Liu, W.J., Lin, L.D.: Histogram-Based Fuzzy Filter for Image Restoration. IEEE Trans. Syst., Man, Cybern. B 32(2), 230–238 (2002)
Lee, C.-S., Guo, S.-M., Hsu, C.-Y.: A novel fuzzy filter for impulse noise removal. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3174, pp. 375–380. Springer, Heidelberg (2004)
Lee, C.-S., Guo, S.-M., Hsu, C.-Y.: Genetic-Based Fuzzy Image Filter and its Applications to Image Processing. IEEE Trans. Syst., Man, Cybern. B 35(4), 694–711 (2005)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simocelli, E.P.: Image Quality Assessment: from Error Visibility to Structural Similarity. IEEE Trans. on Image Processing 13(3), 1–14 (2004)
Hussain, A., Arfan Jaffar, M., Mirza, A.M., Chaudary, A.: Detail Preserving Fuzzy Filter (accepted)
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Hussain, A., Jaffar, M.A., Siddiqui, A.B., Nazir, M., Mirza, A.M. (2009). Modified Histogram Based Fuzzy Filter. In: Gagalowicz, A., Philips, W. (eds) Computer Vision/Computer Graphics CollaborationTechniques. MIRAGE 2009. Lecture Notes in Computer Science, vol 5496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01811-4_25
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DOI: https://doi.org/10.1007/978-3-642-01811-4_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01810-7
Online ISBN: 978-3-642-01811-4
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