Speckle Reduction in Synthetic Aperture Radar Images in Wavelet Domain Using Laplace Distribution
Why this work is in the frame
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Bibliographic record
Abstract
Speckle is a granular noise-like phenomenon which appears in Synthetic Aperture Radar (SAR) images due to coherent properties of SAR systems. The presence of speckle complicates both human and automatic analysis of SAR images. As a result, speckle reduction is an important preprocessing step for many SAR remote sensing applications. Speckle reduction can be made through multi-looking during the image formation or using spatial filters as a preprocessing step. However, these methods have some limitations such as a decrease in spatial resolution or smoothening of details and edges. To overcome these problems, Multi-Resolution Analysis (MRA), such as wavelet transform, should be used. In this paper, a despeckling method based on the Bayesian theory and Maximum a Posteriori (MAP) estimator in the wavelet domain was proposed. The noise-free wavelet coefficients of the logarithmically transformed image and the noise in the wavelet domain were modeled based on the Laplace and Gaussian distributions respectively. VisuShrink, SureShrink, and BayesShrink methods were also implemented and applied to both simulated and real SAR data for comparison purpose and to assess the proposed method. PSNR and beta edge preserving index were used to evaluate the performance of simulated SAR data, while ENL was employed to evaluate the real SAR data. Experimental results of despeckling showed the superior performance of the proposed method in suppressing the speckle efficiently and preserving better the spatial details in the SAR image
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it