Image Edge Detection Algorithm Research Based on the CNN's Neighborhood Radius Equals 2

Edge is one of the basic characteristics of image. Edge detection is a very important step in image analysis, and the cellular neural network is a method that is very effective in edge detection. This article is based on cellular neural networks (cellular neural network, CNN), researching the algorithm of CNN's neighborhood radius equal 2 about the process of image edge detection, expounds the key steps in the process of algorithm realization, and prove the stability of the algorithm. The result based on the neighborhood radius equal 2 of CNN algorithm compare with the CNN's neighborhood radius equal to 1 algorithm and classical algorithm (prewit, cannyt, sobel, etc.), and we can analyze and compares the advantage and disadvantage of several kinds of algorithm on the performance, the accuracy of the quantitative comparison of the test results. The experimental results show that the CNN template algorithm of edge detection based on the neighborhood radius equal 2 results are more significantly, and able to high-speed parallel computing in hardware implementation, can achieve real-time image processing.