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Proceedings Paper

Intelligent detection of impulse noise using multilayer neural network with multi-valued neurons
Author(s): Igor Aizenberg; Glen Wallace
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Paper Abstract

In this paper, we solve the impulse noise detection problem using an intelligent approach. We use a multilayer neural network based on multi-valued neurons (MLMVN) as an intelligent impulse noise detector. MLMVN was already used for point spread function identification and intelligent edge enhancement. So it is very attractive to apply it for solving another image processing problem. The main result, which is presented in the paper, is the proven ability of MLMVN to detect impulse noise on different images after a learning session with the data taken just from a single noisy image. Hence MLMVN can be used as a robust impulse detector. It is especially efficient for salt and pepper noise detection and outperforms all competitive techniques. It also shows comparable results in detection of random impulse noise. Moreover, for random impulse noise detection, MLMVN with the output neuron with a periodic activation function is used for the first time.

Paper Details

Date Published: 2 February 2012
PDF: 12 pages
Proc. SPIE 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II, 82950S (2 February 2012); doi: 10.1117/12.907639
Show Author Affiliations
Igor Aizenberg, Texas A&M Univ.-Texarkana (United States)
Glen Wallace, Texas A&M Univ.-Texarkana (United States)

Published in SPIE Proceedings Vol. 8295:
Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II
Karen O. Egiazarian; John Recker; Guijin Wang; Sos S. Agaian; Atanas P. Gotchev, Editor(s)

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