A New Wavelet-based Method-for Despeckling SAR Images
Bibliographic record
Abstract
In this paper, a wavelet-based despeckling method is proposed for suppressing speckle noise in synthetic aperture radar (SAR) images. A new threshold is proposed to classify the wavelet coefficients into significant and insignificant ones. Local statistic in the wavelet domain is used to further classify the significant coefficients into the edge and non-edge coefficients. The edge coefficients remain unaltered, whereas the non-edge and the insignificant ones are reduced in magnitude. Experiments are carried out on a noise-free image corrupted with simulated speckle noise, and a real SAR image. The results show that the proposed method provides a performance better than that of other methods in terms of the peak signal-to-noise ratio and ability to suppress speckle in the homogeneous areas. In addition, it introduces a bias that is much smaller than that of the other methods as well as preserves edges quite well.
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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 | 0.001 | 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".