  This paper presents two approaches for filter design based on stochastic
distances for intensity speckle reduction. A window is defined around each
pixel, overlapping samples are compared and only those which pass a
goodness-of-fit test are used to compute the filtered value. The tests stem
from stochastic divergences within the Information Theory framework. The
technique is applied to intensity Synthetic Aperture Radar (SAR) data with
homogeneous regions using the Gamma model. The first approach uses a
Nagao-Matsuyama-type procedure for setting the overlapping samples, and the
second uses the nonlocal method. The proposals are compared with the Improved
Sigma filter and with anisotropic diffusion for speckled data (SRAD) using a
protocol based on Monte Carlo simulation. Among the criteria used to quantify
the quality of filters, we employ the equivalent number of looks, and line and
edge preservation. Moreover, we also assessed the filters by the Universal
Image Quality Index and by the Pearson correlation between edges. Applications
to real images are also discussed. The proposed methods show good results.
