Wavelet‐based blind deconvolution of near‐field ultrasound scans
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Bibliographic record
Abstract
A wavelet‐based technique for blind deconvolution and denoising of ultrasound scans is introduced. The target application is near‐field ultrasound imaging for non‐destructive testing. Existing blind deconvolution techniques for ultrasound such as cepstrum‐based methods and the work of Adam and Michailovich – based on discrete wavelet transform (DWT) shrinkage of the log‐spectrum – estimate the pulse by exploiting the pulse log‐spectrum smoothness relative to the material reflectivity function. In the proposed technique, the log‐spectrum is localised with respect to time as the continuous wavelet transform (CWT) log‐scalogram to deal with the non‐stationarity of the near‐field ultrasound signals in both the pulse estimation and deconvolution. The pulse is estimated in the wavelet domain via DWT shrinkage of the log‐scalogram and is deconvolved by wavelet‐domain Wiener filtering. Extensions of the proposed technique include: using separate CWT domains for estimation and deconvolution, as inspired by the WienerChop denoising method; and training the algorithm parameters on a subset of scans.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it