Edge Extraction Based on Memristor Cell Neural Network with Fractional Order Template

Abstract

In order to extract more texture and detail information from a given image, a memristor cell neural network is proposed by replacing the state resistances of the neurons as the memristors. The characteristic of memristor could be maintained in the whole information processing of the cell neural network so that the historical state information could be used and the information processing ability of the network could be enhanced. Furthermore, based on the fractional order calculus theory, a fractional order control template is designed for the memristor cell neural network to enhance the middle-and high-frequency information and retain more low-frequency texture information in the image edge extraction. The network could be used to extract edge information from various kinds of images. The simulation results show that the edge images extracted by this method have more complete and clear contour information and more abundant texture detail information. Both the average gradient and the information entropy of the edge images could be significantly improved.