  Stack filters are a special case of non-linear filters. They have a good
performance for filtering images with different types of noise while preserving
edges and details. A stack filter decomposes an input image into stacks of
binary images according to a set of thresholds. Each binary image is then
filtered by a Boolean function, which characterizes the filter. Adaptive stack
filters can be computed by training using a prototype (ideal) image and its
corrupted version, leading to optimized filters with respect to a loss
function. In this work we propose the use of training with selected samples for
the estimation of the optimal Boolean function. We study the performance of
adaptive stack filters when they are applied to speckled imagery, in particular
to Synthetic Aperture Radar (SAR) images. This is done by evaluating the
quality of the filtered images through the use of suitable image quality
indexes and by measuring the classification accuracy of the resulting images.
We used SAR images as input, since they are affected by speckle noise that
makes classification a difficult task.
