Statistical, neural, and combined filtering algorithms for applicative image noise

Abstract

Correlations for the lower limit of the estimate error variance of images distorted by additive and applicative noise are obtained. The potential and actual estimate error variances for linear and nonlinear filtering algorithms are analyzed. A combined filtering algorithm for images distorted by applicative noise is developed.