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: (I) 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.