The Analysis of Noise Level of RGB Image Generated Using SOM

Sergejs Kodors, Peter Grabusts

Abstract


The article discusses how to generate RGB images with noise using Kohonen’s self-organizing map (SOM). The article also describes the adaptation process and structure of SOM, which can be used to generate RGB images with noise. The authors of the article evaluate the influence of SOM parameters (a learning coefficient, adaptation time, effective width) on the noise level of RGB image generated using SOM. According to these observations, the authors formulate several recommendations how to control the noise level by adjusting SOM parameters.

Keywords:
Additive noise; image generation; self-organizing map

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References


S. J. Sangwine, R. E. N. Horne, The Color Image Processing Handbook, 1st ed. London: Chapman & Hall, 1998, 440p.

С. Хайкин, Нейронные сети: полный курс, 2-е изд., испр. : Пер. с англ. – Москва: ООО «И.Д. Вильямс», 2006, 1104 с.

Л. Н. Ясницкий, Введение в искусственный интеллект. Москва: Издательский центр «Академия», 2005, 176 с.

B. Curry, P. H. Morgan, Evaluating Kohonen’s learning rule: An approach through genetic algorithms, in European Journal of Operational Research, vol. 154, 2004, pp. 191-205.

J. Pęksiński, G. Mikołajczak, Removing Noise from Digital Images Using of Mean Square Approximation, in Procedia Environmental Sciences, vol. 10, 2011, 980-985.

C. C. Yang, H. Chen, K. Hong, Visualization of large category map for Internet browsing, in Decision Support Systems, vol. 35, 2003, pp. 89-102.

L. Yang, Z. Ouyang, Y. Shi, A Modified Clustering Method Based on Self-Organizing Maps and Its Applications, in Procedia Computer Science, vol. 9, 2012, pp. 1371-1379.


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Copyright (c) 2012 Sergejs Kodors, Peter Grabusts

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