High-fidelity image interpolation using radial basis function neural networks

Abstract

Image interpolation using radial basis function (RBF) neural networks is accomplished. In this work the RBF network is first trained with the given image, satisfying the constraint of the gray value at each pixel. With the desired magnification ratio, each pixel is then divided into subpixels. The subpixel gray values are calculated using the trained network. Two dimensional Gaussian basis functions are used as the neurons in the hidden layer.