Duality theory of cellular neural networks for image compression and regeneration

Image compression and regeneration by nonlinear associative cellular neural networks (CNNs) that can be used as retina chips are addressed. One is a sparse Hopfield-type neural network that is called an H-type CNN and the other is its dual network and is called a DH-type CNN. Their input information sequences are given by nodes and links as voltages and currents, respectively. Their error correcting capacity (minimum basins of attraction) is decided by the minimum numbers of links of cutset and loop, respectively. Simulation results showing the performance of both types of network are reported.<
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