Noise density estimation using neural networks

Abstract

A neural network for estimation of unknown noise densities and their gradients is presented. The network structure is similar to a radial basis function. The learning rule is, however, different and has an unsupervised nature that ensures a valid probability density. The algorithm is fast and provides good estimates of noise densities. One and two dimensional examples are reported.<>