Multi-scale contrast-to-noise ratio (MS-CNR): a novel metric for quantitative defect characterisation without manual region specification
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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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".