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, cilt.3497, ss.679-681, 2005 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 3497
  • Basım Tarihi: 2005
  • Dergi Adı: ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 2, PROCEEDINGS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED)
  • Sayfa Sayıları: ss.679-681
  • Erciyes Üniversitesi Adresli: Evet

Özet

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.