A combined speckle noise reduction and, compression of SAR images using a multiwavelet based method to improve codec performance
Bibliographic record
Abstract
SAR images are corrupted by multiplicative noise (speckle) which limits the performance of the classical coder/decoder (codec) in the spatial domain. Our objective is to give an evaluation of the efficiency of a multiwavelet transform coding algorithm. We use the additional degree of freedom offered by multiwavelets to fine tune the number of vanishing moments and the approximation order of their basis functions. Once the multiwavelet transform is performed, we apply an optimal bit allocation scheme on the subbands data using a set of vector quantizers. The quantization of the high frequencies multiwavelets coefficients may be though of as a hard thresholding algorithm. A measure of the equivalent number of looks is performed in the reconstructed SAR image in order to evaluate the impact of the codec in the noise reduction process. We compare our method with classical algorithm (baseline scalar wavelet transform followed by an optimal scalar quantization). The codec achieves comparable SNR, but performs surprising speckle noise reduction. Some results are presented with ERS-PRI images of Cameroon which can be compressed at 20 : 1 while still remaining of sufficient quality for visual interpretation, segmentation and land use monitoring.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".