Pixel-Wise Signal-to-Noise Ratio: A Novel Metric for Quantifying the Detectability of Targets in Infrared Images
Bibliographic record
Abstract
In this paper, a new Signal-to-Noise Ratio (SNR) metric is proposed to quantify the detectability of targets in infrared (IR) images. The proposed metric is based on the contrast between the target and the background, which is consistent with human perception in terms of distinguishing the target from the background, rather than the raw intensity values of the target. In the contrast calculation, individual contribution of each pixel value of the target is considered in the proposed metric, whereas the mean or a single representative raw intensity value of the target is taken into account in the existing metrics. As subjective evaluations are the most precise tools for distinguishing the target from the background, SNR metrics used for IR images are expected to be as consistent as possible with the human visual system. That is, due to its high contrast sensitivity, the human visual system responds to stimuli by cognitively distinguishing the target from the background. Therefore, human perceptioninspired target distinguishability metrics aim to quantify the target detectability consistent with the human visual system, which is capable of distinguishing very small differences in contrast. Extensive performance evaluation tests on well-known IR image datasets, VIVID, SENSIAC and AMCOM, and synthetic image sets demonstrate that the proposed pixel-wise SNR metric quantifies target distinguishability from the background more consistently with subjective evaluations than other SNR metrics. Furthermore, the proposed metric is always robust even when the other metrics fail to accurately quantify target distinguishability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".