Stack filter design for image restoration using genetic algorithms

Stack filters are a class of non-linear spatial operators used for noise suppression. Their design is formulated as an optimisation problem and genetic algorithms used to perform the configuration. Applying the mean absolute error (MAE) as the basis of an objective function, the stack filter is used to restore magnetic resonance images corrupted with uncorrelated additive noise from 10% and 50%. The outcomes are compared with the median filter and return a smaller MAE for all noise levels. The design is extended from 9 point to 13 point filters and by training on Poisson noise, the filter is applied to nuclear medicine bone scans where no absolute truth exists. Image profiles and relative contrast show the filter´s value in reducing noise whilst preserving contrast