Ultrasound Speckle Reduction in the Complex Wavelet Domain
Why this work is in the frame
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Bibliographic record
Abstract
Ultrasound is a non-invasive, portable, and low cost imaging modality that offers real-time image formation and has many applications in medicine. Unfortunately, ultrasound images are inherently degraded by a multiplicative noise called speckle that makes further analysis difficult. As a result, a vast number of ultrasound despeckling methods have been introduced. One of the most successful multiscale Bayesian techniques is based on modeling the wavelet coefficients of the logarithmically transformed ultrasound images using a SαS prior. These improvements can be explained by two special characteristics of DTCWT; DTCWT is approximately shift invariant and it has better directional selectivity compared to standard wavelet transforms. Therefore, the DTCWT is proposed as a good candidate for ultrasound despeckling.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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