Neuralizing target superresolution algorithms

Tatem et al. (2001) have designed a Hopfield network-based algorithm for superresolving discrete targets that are larger than the sample spacing of an image. The algorithm iteratively minimizes a criterion function that contains a sigmoidal activation term. We have altered their algorithm to bring it in line with Hopfield´s original network by reducing the pseudotemperature of the sigmoid. We found that smaller values of the pseudotemperature lead to faster convergence to a solution and resulting solutions that are more accurate.