Edge is an important feature of images. There are many ways to detect the edge of animage. In this paper

the cellular neural network is proposed for edge detection. Cellular neural network is a large scale nonlinear analog circuit suitable for real-time signal and image processing. The key problem is to find a set of parameters for the network. The high-pass filter is utilized to design the parameters of cellular neural network for detecting the binary images. A gray-scale image can be divided into 2 binary planes with different gray level. The edge of gray-scale images then can be detected through synthesizing the edge of each binary plane. Finally

the edge detection result of CNN is compared with that of Sobel and Log algorithms It can be seen from the simulation results that the proposed method is effective. Besides

because the cellular neural networks can use high-speed parallel computation and is easy to be implemented in hardware

therefore it has more potential in real-time image processing.