Characterization and Simple Fixed Pattern Noise Correction in Wide Dynamic Range “Logarithmic” Imagers
Why this work is in the frame
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Bibliographic record
Abstract
Wide dynamic range logarithmic imagers can render naturally illuminated scenes while preserving detail and contrast information at a lower cost than high dynamic range linear sensors. However, the quality of the output is severely degraded by fixed pattern noise (FPN). Although previous FPN correction techniques can eliminate the dominant additive form of this noise, the contrast threshold of the imager over a wide illumination range is poor compared to the human visual system. In this paper, it is shown that a four-parameter model fits the measured characteristic response of wide dynamic range pixels over 11 decades of input current. A comparison of the responses of 200 pixels shows that there are significant variations in all four parameters. A procedure is described that allows the four pixel parameters to be obtained from the response of each pixel to five input currents. However, a much simpler procedure is shown to correct FPN, leading to a contrast threshold comparable to the human visual system over the five decades required to image wide-dynamic-range, naturally illuminated scenes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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