1 Introduction When dealing with problems such as image restoration, there are a number of methods developed from classical concepts, such as deconvolution or some other filtering techniques. However, recent developments in the neural network field attracted our attention, and led us to explore th application of neural networks to image processing problems. The specific problem was to design a method able to correct spherically aberrated images. That is, having and image with optical defects, we would like to have a system that could take this optically aberrated image as an input, correct the problems and have a restored image as an output. However, a neural network approach was studied because of the particular way these systems perform.
Neural networks learn how to solve problems.
Artificial Neural Networks provide less cumbersome solutions than those offered by other rather conventional techniques. A neural network is not programmed, but rather taught to perform a certain task or to solve a type of problem . A database representing a specific problem is used as a source of training data for the network. A trained neural network may be able to solve cases similar to those facts represented by the training database. After a neural network has been trained successfully and tested, it can be applied to a reconstruction of problems similar to those in the training database.