Characterization of Gain Enhanced In-Field Defects in Digital Imagers
Why this work is in the frame
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Bibliographic record
Abstract
The quality of images produced by a digital imager is degraded by the presence of defects, mainly hot pixels, which develop continuously during the imager's lifetime. We previously studied the spatial and temporal distributions of these defects (at ISO 400) and concluded that they most likely result from random radiation and are not material related. With the advancement in imaging technology, the noise level at high ISO had been overcome and new cameras have a wider ISO range (ISO 100-6400). ISO gain is applied to all pixels, good or defective; thus defect parameters get amplified, causing defects to become more visible at high ISO settings. Preliminary defect identification with high ISO has revealed 2 to 3 times more defects at ISO 1600 compared to the standard ISO 400 setting. Amplification of the defect parameters causes defects to become more distinguishable relative to the background noise level. In fact, by measuring the distribution of defect parameters, our experiment results suggest that 2-3% of the faulty pixels behave as stuck-high defects at ISO 1600. With more defects found at higher ISO, we gain a more complete map of defects from each sensor and thus improve our statistical analysis of the spatial and temporal defect distributions. Our current results show that although more defects were found in the tested sensors, the defects are very small and not clustered, pointing to a random defect source rather than a material related one.
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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