Using an exact radial basis function artificial neural network for impulsive noise suppression from highly distorted image databases

Authors:
Pınar Çivicioğlu;Mustafa Alçı;Erkan Beṣdok
Affiliations:
Civil Aviation School, Avionics Dept., Erciyes University, Kayseri, Turkey;Engineering Faculty, Electronic Engineering Dept., Erciyes University, Kayseri, Turkey;Engineering Faculty, Geodesy and Photogrammetry Engineering Dept., Erciyes University, Kayseri, Turkey
Venue:
ADVIS'04 Proceedings of the Third international conference on Advances in Information Systems
Year:
2004

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Abstract

In this paper, a new filter, RM, which is based on exact radial basis function artificial neural networks, is proposed for the impulsive noise suppression from highly distorted images. The RM uses Chi-Squared based goodness-of-fit test in order to find corrupted pixels more accurately.The proposed filter shows a high performance at the restoration of images distorted by impulsive noise. The extensive simulation results show that the proposed filter achieves a superior performance to the other filters mentioned in this paper in the cases of being effective in noise suppression and detail preservation, especially when the noise density is very high.