  Image superresolution methods process an input image sequence of a scene to
obtain a still image with increased resolution. Classical approaches to this
problem involve complex iterative minimization procedures, typically with high
computational costs. In this paper is proposed a novel algorithm for
super-resolution that enables a substantial decrease in computer load. First, a
probabilistic neural network architecture is used to perform a scattered-point
interpolation of the image sequence data. The network kernel function is
optimally determined for this problem by a multi-layer perceptron trained on
synthetic data. Network parameters dependence on sequence noise level is
quantitatively analyzed. This super-sampled image is spatially filtered to
correct finite pixel size effects, to yield the final high-resolution estimate.
Results on a real outdoor sequence are presented, showing the quality of the
proposed method.
