  Image superresolution involves the processing of an image sequence to
generate a still image with higher resolution. Classical approaches, such as
bayesian MAP methods, require iterative minimization procedures, with high
computational costs. Recently, the authors proposed a method to tackle this
problem, based on the use of a hybrid MLP-PNN architecture. In this paper, we
present a novel superresolution method, based on an evolution of this concept,
to incorporate the use of local image models. A neural processing stage
receives as input the value of model coefficients on local windows. The data
dimensionality is firstly reduced by application of PCA. An MLP, trained on
synthetic sequences with various amounts of noise, estimates the
high-resolution image data. The effect of varying the dimension of the network
input space is examined, showing a complex, structured behavior. Quantitative
results are presented showing the accuracy and robustness of the proposed
method.
