Memristor CNN Model for Image Denoising In this paper we develop an algorithm for noise removal which extracts the target information (image) more precisely. The model is based on the convection direction during time evolution in order to diffuse the noise. For this purpose we propose a convection  diffusion system in which the convection term is added in the modified diffusion partial differential equation (PDE) and it serves as physical interpretation for removing the noise. We introduce memristor CNN (MCNN) model in order to make the numerical computations accurate. The proposed MCNN architecture has several advantages as high density, nonvolatility and programmability of synaptic weights. Finally, we provide simulations and validation on image denoising applications. In this way we show that our model is better in noise removing through several numerical experiments.

Theory of Cellular Nonlinear Networks with Analogue Dynamic Memristors Cellular Nonlinear Networkbased visual microprocessors offer the opportunity to process and store data at the detection place, but suffer from low spatial resolution due to the allocation of memory units to each cell. Memristor technologies offer the opportunity to resolve this issue. In fact, nonvolatile memristors, employed in a novel cell design, may contribute to process data, and serve as memory units. Using a memristor with scalar state in each cell calls for the generalization of the Dynamic Route Map technique for analysing and designing arrays with secondorder processing elements. This novel systemtheoretical concept is introduced here through illustrative examples.