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Using LM Artificial Neural Networks and η-Closest-Pixels for Impulsive Noise Suppression from Highly Corrupted Images

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

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Abstract

In this paper, a new filter, ηLM, which is based on Levenberg-Marquardt Artificial Neural Networks, is proposed for the impulsive noise suppression from highly distorted images. The ηLM 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

  • eBook Packages: Computer ScienceComputer Science (R0)

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