Gabor-Type Filtering using Transient States of Cellular Neural Networks

Gabor filtering is useful for intelligent image processing, but it requires huge computational power. Its pixel-parallel LSI implementation is one solution for real-time image processing. This paper proposes a new Gabor filtering algorithm using a discrete-time cellular neural network (CNN) circuit, which is suitable for pixel-parallel LSI implementation. The proposed algorithm utilizes transient states of the CNN to obtain Gabor coefficients, and it has the following advantages: (1) the amplitudes of all coefficients of the terms in the dynamics equations are on the same order; (2) the relative amplitudes of Gabor coefficients can arbitrarily be controlled; and (3) the number of calculation steps required for obtaining Gabor coefficients can be reduced compared with the algorithm using the steady state; (4) the window function of the filter is nearly Gaussian.