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An algorithm called ICM-TM is proposed to reduce the effect of Gaussian noise in grayscale images based on a kind of Artificial Neural Networks type Pulse-Coupled simplified called Intersection Cortical Model (ICM). A Time Matrix (TM) provides the respective information to the iteration number where first activated neuron corresponding to each pixel; a selective filtering criteria is established combining the median and average operators based on the neurons activation times. The performance of the proposed algorithm was evaluated experimentally with varying degrees of Gaussian noise and the results of the simulations show that the effectiveness of the method is superior to the median filter, Wiener filter and to the Pulse-Coupled Neural Networks with the Null 45 Research in Computing Science 114 (2016) pp. 45–58; rec. 2016-03-11; acc. 2016-05-21 Interconnections (PCNNNI); the results are mainly represented by the parameter Peak Signal to Noise Ratio (PSNR).

Uso de redes neuronales pulsantes para mejorar el filtrado de imágenes contaminadas con ruido Gaussiano