Abstract
In this paper, using hypergraph theory, we introduce an image model called Adaptive Image Neighborhood Hypergraph (AINH). From this model we propose a combinatorial definition of noisy data. A detection procedure is used to classify the hyperedges either as noisy or clean data. Similar to other techniques, the proposed algorithm uses an estimation procedure to remove the effects of the noise. Extensive simulations show that the proposed scheme consistently works well in suppressing of impulsive noise.
This work has been supported by the project ‘Pars_CNR n∘ 36’ and the ‘comité mixte inter_universitaire franco_marocain under grant AI n∘ 166/SI/98’
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References
Berge, C.: Graphs North Holland (1987).
Berge, C.: Hypergraphs. North-Holland, Amsterdam (1987)
Bretto, A., Azema, Cherifi, H., Laget, B.: Combinatorics and image processing. Computer Vision Graphic in Image Processing, 59(5), September (1997) 256–277
Bretto, A., Cherifi, H.: A noise cancellation algorithm based on hypergraph modeling. IEEE Workshop on Digital Signal Processing (1996) 1–4.
Bretto, A., Cherifi, H.: Noise detection and cleaning by hypergraph model. In: IEEE Computer Sciences (ed.): International Symposium on Information Technology: Coding and computing. IEEE Computer Sciences (2000) 416–419
Chen, T., Wu, H.R.: Adaptive Impulse Detection Using Center Weighted Median Filters. IEEE Signal Processing Letters 8 (2001) 1–3
Chen, T., Wu, H.R.: Application of Partition-Based Median Type Filters for Suppressing Noise in Images. (accepted) IEEE Transactions on Image Processing (2001)
Gondran, M., Minoux: Graphs and Algorithms. Wiley, Chichester (1984)
Kovalevsky, V.A.: Finite topology as applied to image processing. Computer vision Graphics, and Image Processing. 46 (1989) 141–161
Macchi, O., Faure, C., Caliste, J.P.: Probabilités d’erreur de détecteurs. Revue CETHEDEC, Cah. NS 75 (1975) 1–51
Rital, S., Bretto, A., Cherifi, H., Aboutajdine, D.: Modélisation d’images par hypergraphe application au Débruitage. ISIVC Rabat (2000)
Rosenfeld, A.: Digital Image Processing. Academic Press, San Diego (1982)
Russ, J.C.: The image processing handbook. CRC Press, IEEE press. Berlin Springer (1999)
Stevenson, R.L., Schweizer, S.M.: Nonlinear Filtering Structure for Image Smoothing in Mixed-Noise Environments. Journal of Mathematical Imaging and Vision, 2 (1992) 137–154
Tovar, R., Esleves, C., Rustarnante, R., Psenicka, B., Mitra, S.K.: Implementation of an Impulse Noise Removal Algorithm from Images. (2000) preprint.
Voss, K.: Discrete Images, Object and Functions in ℤn. Algorithms and Combinatorics. Springer Verlag (1990)
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Rital, S., Bretto, A., Aboutajdine, D., Cherifi, H. (2001). Application of Adaptive Hypergraph Model to Impulsive Noise Detection. In: Skarbek, W. (eds) Computer Analysis of Images and Patterns. CAIP 2001. Lecture Notes in Computer Science, vol 2124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44692-3_67
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DOI: https://doi.org/10.1007/3-540-44692-3_67
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