A Bayesian Joint Decorrelation and Despeckling approach for speckle reduction of SAR Images
Bibliographic record
Abstract
<p>In this paper, we present a novel approach for joint decorrelation<br />and despeckling of synthetic aperture radar (SAR) imagery. An iterative<br />maximum a posterior estimation is performed to obtain the<br />correlation and speckle-free SAR data, which incorporates a correlation<br />model which realistically explores the physical correlated<br />process of speckle noise on signal in SAR imaging. The correlation<br />model is determined automatically via Bayesian estimation in the<br />log-Fourier domain and patch-wise computation is used to account<br />for spatial nonstationarities existing in SAR data. The proposed<br />approach is compared to a state-of-the-art despeckling technique<br />using both simulated and real SAR data. Experimental results illustrate<br />its improvement in preserving the structural detail, especially<br />the sharpness of the edges, when suppressing speckle noise.</p>
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".