Intelligent detection of impulse noise using multilayer neural network with multi-valued neurons

Igor Aizenberg; Glen Wallace

doi:10.1117/12.907639

2 February 2012 Intelligent detection of impulse noise using multilayer neural network with multi-valued neurons

Igor Aizenberg, Glen Wallace

Proceedings Volume 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II; 82950S (2012) https://doi.org/10.1117/12.907639
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

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.

Citation Download Citation

Igor Aizenberg and Glen Wallace "Intelligent detection of impulse noise using multilayer neural network with multi-valued neurons", Proc. SPIE 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II, 82950S (2 February 2012); https://doi.org/10.1117/12.907639

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