Image processing with the random neural network (RNN)

We discuss novel approaches for image enlargement and fusion using the RNN, after successful results with still and video compression and image segmentation. In the RNN model signals in the form of spikes of unit amplitude circulate among the neurons. Positive signals represent excitation and negative signals represent inhibition. Each neuron's state is a non-negative integer called its potential, which increases when an excitation signal arrives to it, and decreases when an inhibition signal arrives. An excitatory spike is interpreted as a "+1" signal at a receiving neuron, while an inhibitory spike is interpreted as a "-1" signal.