Using Pulse Coupled Neural Networks to Improve Image Filtering Contaminated with Gaussian Noise

Authors

  • Estela Ortiz Rangel Centro Nacional de Investigación y Desarrollo Tecnológico, Departamento de Ciencias Computacionales, Cuernavaca, Morelos
  • Manuel Mejía-Lavalle Centro Nacional de Investigación y Desarrollo Tecnológico, Departamento de Ciencias Computacionales, Cuernavaca, Morelos
  • Humberto Sossa Instituto Politécnico Nacional, Centro de investigación en Computación, Ciudad de México

DOI:

https://doi.org/10.13053/cys-21-2-2742

Keywords:

Intersection Cortical Model (ICM), Gaussian noise, Wiener filter, Peak Signal to Noise Ratio (PSNR).

Abstract

An algorithm called ICM-TM to reduce the effect of Gaussian noise in grayscale images isproposed. It is based on the operation of the well- known Intersection Cortical Model (ICM), a kind of Pulse-Coupled Artificial Neural Network. A Time Matrix (TM) provides information about the iteration when the neuron fires for first time. Each neuron corresponds to a pixel. A selective filtering criteria that combines the median and average operators using the neuron´sactivation time is established. The performance of the proposed algorithm is evaluated experimentally with varying degrees of Gaussian noise. Simulation results show that the effectiveness of the method is superior to the median filter, Gaussian filter, Sigma filter, Wiener filter and to the Pulse-Coupled Neural Networks with the Null Interconnections (PCNNNI). Results are mainly provided by the parameter Peak Signal to Noise Ratio (PSNR).

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Published

2017-06-30

Issue

Vol. 21 No. 2 (2017): Thematic issue: Authorship Analysis (Guest editors: M. Montes, L. Villaseñor)

Section

Articles

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