Integration of an Adaptive Cellular Automaton and a Cellular Neural Network for the Impulsive Noise Suppression and Edge Detection in Digital Images

Abstract

This article proposes the combination of two bio-inspired computational models that are sequentially implemented to eliminate impulsive noise and edge detection in grayscale images. In general, this procedure consists of: (1) implementing a cellular automaton (CA) with an adaptive behavior that expands the Moore neighborhood when it considers that the information obtained from its first level neighbors is insufficient. Based on the above, the image affected by noise is processed, in order to eliminate the corrupted pixels and perform reprocessing that will lead to the improvement of the quality of the image, (2) the resulting image is defined as an input of the cellular neural network (CNN) together with the training images, so that by defining three templates (feedback (A), cloning (B) and threshold or bias (I)), contour detection of objects within the image thrown by the initial method can be performed. The results for the noise elimination present a restoration of the image that oscillates between 70.63% and 99.65%, indicating that the image does not lose its quality despite being exposed to high noise levels, similarly it occurs for the edge detection, which presents an approximate efficiency of 65% with respect to the algorithms established within the framework of comparison.