Using LM artificial neural networks and η-closest-pixels for impulsive noise suppression from highly corrupted images

Authors:
Pınar Çivicioǧlu
Affiliations:
Erciyes University, Civil Aviation School, Department of Aircraft Electrics and Electronics, Kayseri, Turkey
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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Abstract

In this paper, a new filter, η-LM, which is based on Levenberg-Marquardt Artificial Neural Networks, is proposed for the impulsive noise suppression from highly distorted images. The η - LM uses Anderson-Darling goodness-of-fit test in order to find corrupted pixels more accurately. 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 detail preservation and noise suppression, especially when the noise density is very high.