This paper describes a neural network based multiscale image restoration approach. Multilayer perceptrons are trained
with artificial images of degraded gray level circles, in an attempt to make the neural network learn inherent space
relations of the degraded pixels. The present approach simulates the degradation by a low pass Gaussian filter blurring
operation and the addition of noise to the pixels at pre-established rates. The training process considers the degraded
image as input and the non-degraded image as output for the supervised learning process. The neural network thus
performs an inverse operation by recovering a quasi non-degraded image in terms of least squared. The main difference
of the approach to existing ones relies on the fact that the space relations are taken from different scales, thus providing
relational space data to the neural network. The approach is an attempt to come up with a simple method that leads to an
optimum solution to the problem. Considering different window sizes around a pixel simulates the multiscale operation.
In the generalization phase the neural network is exposed to indoor, outdoor, and satellite degraded images following the
same steps use for the artificial circle image.
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