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


Civicioglu P.

ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 2, PROCEEDINGS, vol.3497, pp.679-681, 2005 (Journal Indexed in SCI) identifier

  • Publication Type: Article / Article
  • Volume: 3497
  • Publication Date: 2005
  • Title of Journal : ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 2, PROCEEDINGS
  • Page Numbers: pp.679-681

Abstract

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