On the applications of divergence type measures in testing statistical hypotheses
Journal of Multivariate Analysis
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
The Split Bregman Method for L1-Regularized Problems
SIAM Journal on Imaging Sciences
Iterative weighted maximum likelihood denoising with probabilistic patch-based weights
IEEE Transactions on Image Processing
How to Compare Noisy Patches? Patch Similarity Beyond Gaussian Noise
International Journal of Computer Vision
Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
A novel approach to polarimetric SAR data processing based on Nonlinear PCA
Pattern Recognition
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This paper presents a technique for reducing speckle in Polarimetric Synthetic Aperture Radar (PolSAR) imagery using nonlocal means and a statistical test based on stochastic divergences. The main objective is to select homogeneous pixels in the filtering area through statistical tests between distributions. This proposal uses the complex Wishart model to describe PolSAR data, but the technique can be extended to other models. The weights of the location-variant linear filter are function of the p-values of tests which verify the hypothesis that two samples come from the same distribution and, therefore, can be used to compute a local mean. The test stems from the family of (h-@f) divergences which originated in Information Theory. This novel technique was compared with the Boxcar, Refined Lee and IDAN filters. Image quality assessment methods on simulated and real data are employed to validate the performance of this approach. We show that the proposed filter also enhances the polarimetric entropy and preserves the scattering information of the targets.