Competitively coupled orientation selective cellular neural networks

We extend previous work in orientation selective cellular neural networks to include competitive couplings between different layers tuned to different orientations and spatial frequencies. The presence of these interactions sharpens the spatial frequency tuning of the filters in two ways, when compared to a similar architecture proposed previously which lacks these interactions. The first is the introduction of nulls in the frequency response. The second is the introduction of constraints on the passbands of the coupled layers. Based on an understanding of these two effects, we propose a method for choosing spatial frequency tunings of the individual layers to enhance orientation selectivity in the coupled system.