Bibliographic record
Abstract
Under low-illumination conditions, images inevitably contain both Poisson and Gaussian noise. In electron microscopy, there is the added complication whereby increasing the dose-rate, to improve signal-to-noise, damages the specimen being imaged, making certain materials being impossible to characterise. Conventional data smoothing techniques may dampen usable image contrast, and deep-neural network (DNN) based approaches risk the introduction of artefacts. In this work, the complementary strengths of patch-based and DNN approaches are combined into a lightweight denoising architecture such that experimental data integrity is preserved while effectively removing noise. Our approach, the Rapid Eigenpatch Utility Classifier for Image Denoising (REUCID), leverages the speed and data-integrity of a non-local patch-based SVD step to identify key image components, followed by a convolutional neural network (CNN) acting strictly in a classification capacity on the SVD eigenvectors. This classification-only approach to DNN integration represents a significant advance by mitigating the risk of DNN overreach while maintaining denoising effectiveness. We demonstrate superior performance on high angle annular dark field images, where our hybrid method outperforms conventional techniques in enhancing image contrast while preserving genuine structural features.
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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 | 0.002 | 0.003 |
| 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 | 0.000 |
| 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".