Multi-scale contrast-to-noise ratio (MS-CNR): a novel metric for quantitative defect characterisation without manual region specification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In numerous research domains where imaging plays a pivotal role in analysing specific objects or processes, it is crucial to quantitatively evaluate the performance of acquisition systems and processing algorithms in differentiating the target from its background. This paper presents the Multi-Scale Contrast-to-Noise Ratio (MS-CNR) metric, a novel tool for precise defect quantification across various imaging modalities. The MS-CNR metric employs the Laplacian of Gaussian (LoG) operator to analyse contrast at multiple scales, allowing for effective quantitative defect characterisation without relying on predefined regions for defects or noise. Through comprehensive evaluation with synthetic and real data, the MS-CNR metric demonstrates a strong correlation with human visual perception and other well-established SNR metrics. It provides consistent and reproducible results, outperforming traditional SNR metrics that may be affected by specific types of noise. The MS-CNR metric’s robust performance and alignment with visual assessments make it a valuable addition to imaging analysis, offering a reliable and automated approach for evaluating defect visibility.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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