Contraharmonic filtering using cellular neural networks

We describe methods of designing cellular neural networks (CNNs) for a class of nonlinear filters, referred to as contraharmonic filters. These filters exhibit good performance in filtering images corrupted by impulse noise. The new cellular neural network design uses simple nonlinear templates, suitable for implementation in a locally connected CNN array. The performance of the filter is demonstrated using images corrupted by impulse noise