%A Carlos Miravet
%A Francisco B. Rodriguez
%T Accurate and robust image superresolution by neural processing of local image representations 
%X 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 dimension-ality is firstly reduced by application of PCA. An MLP, trained on synthetic se-quences with various amounts of noise, estimates the high-resolution image data. The effect of varying the dimension of the network input space is exam-ined, showing a complex, structured behavior. Quantitative results are presented showing the accuracy and robustness of the proposed method.
%K superresolution, neural networks, image sequence processing 
%P 499-506
%V 3696
%D 2005
%I Springer-Verlag
%L cogprints4567

<script src="//archive-bar.soton.ac.uk/archive-bar.js"></script>