Comparison of nonlocal means despeckling based on stochastic measures

This work presents the use of stochastic measures of similarities as features with statistical significance for the design of despeckling nonlocal means filters. Assuming that the observations follow a Gamma model with two parameters (mean and number of looks), patches are compared by means of the Kullback-Leibler and Hellinger distances, and by their Shannon entropies. A convolution mask is formed using the p-values of tests that verify if the patches come from the same distribution. The filter performances are assessed using well-known phantoms, three measures of quality, and a Monte Carlo experiment with several factors. The proposed filters are contrasted with the Refined Lee and NL-SAR filters.

[1]  Jean-Michel Morel,et al.  A Review of Image Denoising Algorithms, with a New One , 2005, Multiscale Model. Simul..

[2]  Licheng Jiao,et al.  SAR Image Despeckling Using Bayesian Nonlocal Means Filter With Sigma Preselection , 2011, IEEE Geoscience and Remote Sensing Letters.

[3]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[4]  Luisa Verdoliva,et al.  A Nonlocal SAR Image Denoising Algorithm Based on LLMMSE Wavelet Shrinkage , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Renato J. Cintra,et al.  Analytic Expressions for Stochastic Distances Between Relaxed Complex Wishart Distributions , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Alejandro César Frery,et al.  Speckle Reduction Using Stochastic Distances , 2012, CIARP.

[7]  Florence Tupin,et al.  Iterative Weighted Maximum Likelihood Denoising With Probabilistic Patch-Based Weights , 2009, IEEE Transactions on Image Processing.

[8]  Cristian Munteanu,et al.  Supervised Constrained Optimization of Bayesian Nonlocal Means Filter With Sigma Preselection for Despeckling SAR Images , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Leandro Pardo,et al.  On the applications of divergence type measures in testing statistical hypotheses , 1994 .

[10]  Renato J. Cintra,et al.  Entropy-Based Statistical Analysis of PolSAR Data , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Florence Tupin,et al.  NL-SAR: A Unified Nonlocal Framework for Resolution-Preserving (Pol)(In)SAR Denoising , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Corina da Costa Freitas,et al.  Speckle reduction in polarimetric SAR imagery with stochastic distances and nonlocal means , 2013, Pattern Recognit..

[13]  Thomas L. Ainsworth,et al.  Improved Sigma Filter for Speckle Filtering of SAR Imagery , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Wentao An,et al.  Nonlocal Filtering for Polarimetric SAR Data: A Pretest Approach , 2011, IEEE Transactions on Geoscience and Remote Sensing.