Abstract-When low-level hardware simulations of cellular neural networks (CNN's) are very costly for exploring new applications, the use of a behavioral simulator becomes indispensable. This paper presents a software prototype capable of performing image processing applications using CNN's. The software is based on a CNN multilayer structure in which each primary color is assigned to a unique layer. This allows an added flexibility as different processing applications can be performed in parallel. To be able to handle a full range of color tones, two novel color mapping schemes were derived. In the proposed schemes the color information is obtained from the cell's state rather than from its output. This modification is necessary because for many templates CNN has only binary stable outputs from which only either a fully saturated or a black color can be obtained. Additionally, a postprocessor capable of performing pixelwise logical operations among color layers was developed to enhance the results obtained from CNN. Examples in the areas of medical image processing, image restoration, and weather forecasting are provided to demonstrate the robustness of the software and the vast potential of CNN..

A novel approach to simulate Cellular Neural Networks (CNN) is presented in this paper.The approach, time-multiplexing simulation, is prompted by the need to simulate hardware models and test hardware implementations of CNN. For practical size applications, due to hardware limitations, it is impossible to have a one-on-one mapping between the CNN hardware processors and all the pixels of the image. This simulator provides a solution by processing the input image block by block, with the number of pixels in a block being the same as the number of CNN processors in the hardware. The algorithm for implementing this simulator is also presented, along with some simulation results and comparisons.