  This paper presents a new approach for filter design based on stochastic
distances and tests between distributions. A window is defined around each
pixel, samples are compared and only those which pass a goodness-of-fit test
are used to compute the filtered value. The technique is applied to intensity
Synthetic Aperture Radar (SAR) data, using the Gamma model with varying number
of looks allowing, thus, changes in heterogeneity. Modified Nagao-Matsuyama
windows are used to define the samples. The proposal is compared with the Lee's
filter which is considered a standard, using a protocol based on simulation.
Among the criteria used to quantify the quality of filters, we employ the
equivalent number of looks (related to the signal-to-noise ratio), line
contrast, and edge preservation. Moreover, we also assessed the filters by the
Universal Image Quality Index and the Pearson's correlation between edges.
