Preprocessor embedding in cellular neural network

Abstract

Cellular neural network (CNN) is a highly parallel analog circuit suitable for real‐time image processing. However, the needs of preprocessors in real‐world applications usually destroy the parallel processing nature of CNN. In this paper, we propose methods to embed the function of a preprocessor into a single CNN universal machine (CNN‐UM) such that the novel CNN contains the functions of both the preprocessor and the original CNN. The embedding processes are divided into three categories according to the structure of the B template in CNN. In the general case of nonzero B template (Case 1), an unbiased method and three approximation methods are proposed. The unbiased method is equivalent to its sequential counterpart, although templates of twice the neighborhood size should be used. Among the three approximation methods, AM III shows the least deviation from the unbiased method. In some experiments, AM II and AM III even outperform the unbiased method in noise‐elimination. In the two degenerated cases of Case 2 and Case 3 CNN, the embedding process can be fulfilled with templates of the original size. An exact solution can be derived for Case 2 while an approximate solution is developed for Case 3. The deviation due to the approximation in Case 3 is minor and the assumption made can even assist in noise elimination.