Color image edge extraction using memristor-based CNN

Because of the locally connected lattice structure and high-speed parallel processing, cellular neural network (CNN) have been widely used in image processing. Traditional processing methods typically employ fixed templates, which impose significant limitations on practical complex image processing. However, the hardware implementation of large-scale CNNs becomes impossible due to the bottleneck of traditional CMOS technology. In this paper, a new threshold-adaptive algorithm is proposed by considering pixel space distributions based on human visual perception, which can overcome the aforementioned limitation. Then, the memristor, a two-terminal nonlinear device with unique high-speed switching, nonvolatility, and nanometer scale is used to solve the circuit realization problem. Specifically, we design a spintronic memristor-based CNN (SMCNN) to facilitate the proposed threshold-adaptive algorithm. Finally, by using the example of color image processing, the effectiveness of the proposed SMCNN is demonstrated by means of numerical simulations and comparative analysis.