Neural-network superresolution

The design options of superresolution using a neural network are discussed. Four specific neural-network architectures using different preprocessors are described, and their performances are compared. The correlation transformer is found to be the preprocessor that provides the best performance and the simplest implementation. The correlation transformer converts N complex inputs derived from a phased-array antenna into N(N+1)/2 complex outputs that are applied to the neural-network