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Noise density estimation using neural networks

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4 Author(s)
M. T. Musavi ; Maine Univ., Orono, ME, USA ; D. M. Hummels ; A. J. Laffely ; S. P. Kennedy

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

Published in:

Neural Networks for Signal Processing [1992] II., Proceedings of the 1992 IEEE-SP Workshop

Date of Conference:

31 Aug-2 Sep 1992