Wavelet-based blind deconvolution and denoising of ultrasound scans for non-destructive test applications
Bibliographic record
Abstract
A novel technique for blind deconvolution of ultrasound is introduced. Existing 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 – exploit the smoothness of the pulse log-spectrum relative to the reflectivity function to estimate the pulse. To reduce the effects of non-stationarity in the ultrasound signal on both the pulse estimation and deconvolution, the log-spectrum is time-localized and represented as the Continuous Wavelet Transform (CWT) log-scalogram in the proposed technique. The pulse CWT coefficients are estimated via DWT shrinkage of the log-scalogram and are then deconvolved by wavelet-domain Wiener filtering. Parameters of the technique are found by heuristic optimization on a training set with various quality metrics: entropy, autocorrelation 6-dB width and fractal dimension. The technique is further enhanced by using different CWT wavelets for estimation and deconvolution, similar to the WienerChop method.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".