Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | adaptative filters, information theory, nonlocal means, radar imaging, synthetic aperture radar |
Field | DocType | ISSN |
Computer vision,Monte Carlo method,Speckle pattern,Convolution,Computer science,Synthetic aperture radar,Maximum likelihood,Artificial intelligence | Conference | 2153-6996 |
Citations | PageRank | References |
1 | 0.35 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rafael Grimson | 1 | 18 | 5.27 |
| Natalia S. Morandeira | 2 | 1 | 0.35 |
| Alejandro C. Frery | 3 | 368 | 38.29 |