Filtering of two-dimensional digital images using weighted averaging for adaptive selection of weights

• Autorzy: Momot A. , Wrobel J. , Horoba K. , Jeżewski M. , Bernys M.
• Języki publikacji: EN

Abstrakty

EN

Many digital images, especially in biomedical fields, contain some disturbances. The image analysis depends on quality of the images that is why reduction or elimination (if it is possible) the disturbances is the key issue. There are many methods of improvement in the quality of the images and thus improve the quality of the image analysis, among them one of the simplest method is low-pass filtering such as arithmetic mean or its generalization, weighted mean. The basic problem of the weighted mean is the proper selection of the weights. This can be done using adaptive algorithms. This paper presents several such algorithms which are modifications of the existing weighted averaging methods created originally for noise reduction in electrocardiographic signal. The description of the new filtering methods and a few results of its application are also presented with comparison to existing arithmetic average filtering.

Słowa kluczowe

EN

image filtering; weighted averaging; adaptive selection of weights

PL

filtracja obrazów; ważone uśrednianie

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2012
• Tom: Vol. 20
• Strony: 93--99
• Opis fizyczny: Bibliogr. 8 poz., rys., tab.

Twórcy

autor

Momot A.

• Silesian University of Technology, Institute of Informatics, Akademicka St. 16, 44-100 Gliwice

autor

Wrobel J.

autor

Horoba K.

autor

Jeżewski M.

autor

Bernys M.

Bibliografia

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• [4] GONZALEZ R.C., WOODS R.E., Digital Image Processing. Prentice Hall, Upper Saddle River, New Jersey, 2002.
• [5] MOMOT A., Fuzzy Weighted Averaging of Biomedical Signal Using Bayesian Inference., In: CYRAN K.A. et al. (Eds.) Man-Machine Interactions, Advances in Intelligent and Soft Computing, Springer-Verlag Berlin Heidelberg, 2009, Vol. 59, pp. 133-140.
• [6] MOMOT A., Application of Adaptive Weighed Averaging to Digital Filtering of 2D Images, In: PIETKA E., KAWA J. (Eds.) Information Technologies in Biomedicine (Vol. 2), Advances in Soft Computing, Springer-Verlag Berlin Heidelberg, 2010, Vol. 69, pp. 33–44.
• [7] MOMOT A., Methods of Weighted Averaging with Application to Biomedical Signals, In: GARGIULO G., MCEWAN A. (Eds.) Applied Biomedical Engineering, InTech, Rijeka, Croatia, 2011, pp. 361-386.
• [8] XU Q., MA L., NIE W., PENG L., JIAWAN Z., JIZHOU S., Adaptive Fuzzy Weighted Average Filter for Synthesized Image, In: GERWASI O. et al.(eds.) Proc. Int. Conf. on Computational Science and Its Applications ICCSA 2005, Lecture Notes in Computer Science, Springer, Heidelberg, 2005, Vol. 3482, pp. 292-298.