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.
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Çivicioğlu, P. (2005). Using LM Artificial Neural Networks and η-Closest-Pixels for Impulsive Noise Suppression from Highly Corrupted Images. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_110
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DOI: https://doi.org/10.1007/11427445_110
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
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