Superresolution algorithms for a modified Hopfield neural network

The authors describe the implementation of a superresolution (or spectral extrapolation) procedure on a neural network, based on the Hopfield (1982) model. They show the computational advantages and disadvantages of such an approach for different coding schemes and for networks consisting of very simple two-state elements as well as those made up of more complex nodes capable of representing a continuum. It is demonstrated that, with the appropriate hardware, there is a computational advantage in using the Hopfield architecture over some alternative methods for computing the same solution. The relationship between a particular mode of operation of the neural network and the regularized Gerchberg (1974) and Papoulis (1975) algorithm is also discussed