Abstract
In this paper, a novel filter for high-density salt and pepper noise removal based on the fuzzy mathematical morphology using t-norms is proposed. This filter involves two phases, namely, a detection step of the corrupted pixels and the restoration of the image using a specialized regularization method using fuzzy open-close and close-open sequences. The experimental results show that the proposed algorithm outperforms other nonlinear filtering methods both from the visual point of view and the values of some objective performance measures for images corrupted up to 90% of noise.
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References
Srinivasan, K.S., Ebenezer, D.: A new fast and efficient decision-based algorithm for removal of high-density impulse noises. IEEE Signal Processing Letters 14(3), 189–192 (2007)
Ze-Feng, D., Zhou-Ping, Y., You-Lun, X.: High probability impulse noise-removing algorithm based on mathematical morphology. IEEE Signal Processing Letters 14(1), 31–34 (2007)
Schulte, S., De Witte, V., Nachtegael, M., Van der Weken, D., Kerre, E.E.: Fuzzy two-step filter for impulse noise reduction from color images. IEEE Transactions on Image Processing 15(11), 3567–3578 (2006)
Wang, X., Zhao, X., Guo, F., Ma, J.: Impulsive noise detection by double noise detector and removal using adaptive neural-fuzzy inference system. Int. J. Electron. Commun. 65, 429–434 (2011)
Serra, J.: Image analysis and mathematical morphology, vol. 1, 2. Academic Press, London (1982)
Bloch, I., Maître, H.: Fuzzy mathematical morphologies: A comparative study. Pattern Recognition 28, 1341–1387 (1995)
Nachtegael, M., Kerre, E.E.: Classical and fuzzy approaches towards mathematical morphology. In: Kerre, E.E., Nachtegael, M. (eds.) Fuzzy Techniques in Image Processing. STUDFUZZ, vol. 52, pp. 3–57. Springer, Heidelberg (2000)
González-Hidalgo, M., Mir-Torres, A., Ruiz-Aguilera, D., Torrens, J.: Fuzzy morphology based on uninorms: Image edge-detection. Opening and closing. In: Tavares, J., Jorge, N. (eds.) Computational Vision and Medical Image Processing, pp. 127–133. Taylor & Francis Group (2008)
Papari, G., Petkov, N.: Edge and line oriented contour detection: State of the art. Image and Vision Computing 29(2-3), 79–103 (2011)
Lerallut, R., Decenciére, É., Meyer, F.: Image filtering using morphological amoebas. Image and Vision Computing 25(4), 395–404 (2007)
Maragos, P.: Morphological filtering. In: Bovik, A. (ed.) The Essential Guide to Image Processing, pp. 293–321. Academic Press, Boston (2009)
González-Hidalgo, M., Massanet, S., Mir, A., Ruiz-Aguilera, D.: High-density impulse noise removal using fuzzy mathematical morphology. Accepted in EUSFLAT (2013)
Klement, E.P., Mesiar, R., Pap, E.: Triangular norms. Kluwer Academic Publishers, London (2000)
Baczyński, M., Jayaram, B.: Fuzzy Implications. STUDFUZZ, vol. 231. Springer, Heidelberg (2008)
De Baets, B.: Fuzzy morphology: A logical approach. In: Ayyub, B.M., Gupta, M.M. (eds.) Uncertainty Analysis in Engineering and Science: Fuzzy Logic, Statistics, and Neural Network Approach, pp. 53–68. Kluwer Academic Publishers, Norwell (1997)
Singh, A., Ghanekar, U., Kumar, C., Kumar, G.: An efficient morphological salt-and-pepper noise detector. Int. J. Advanced Networking and Applications 2, 873–875 (2011)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004)
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González-Hidalgo, M., Massanet, S., Mir, A., Ruiz-Aguilera, D. (2013). A Fuzzy Filter for High-Density Salt and Pepper Noise Removal. In: Bielza, C., et al. Advances in Artificial Intelligence. CAEPIA 2013. Lecture Notes in Computer Science(), vol 8109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40643-0_8
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DOI: https://doi.org/10.1007/978-3-642-40643-0_8
Publisher Name: Springer, Berlin, Heidelberg
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