Image Degradation due to Interacting Hot Pixels and SEUs
Bibliographic record
Abstract
Hot pixels (induced by cosmic rays) in digital imaging sensors accumulate with camera age, impacting the quality of all images produced by the camera. During its lifetime, the imager also experiences Single Event Upsets (SEU s) that create transient defects affecting a single image. The SEUs occur at a much higher rate (events/second) compared to 10-100s additional permanent hot pixels that occur per year. We explore in this paper how an SEU that occurs within a close proximity (within a 5×5 pixel area) of an existing Hot Pixel may distort the image. We also consider situations where an SEU impacts two or more adjacent pixels. When such events happen, the currently employed color demosaicing and JPEG image compression algorithms spread the damage into a 16×16 pixel area, creating significant color changes and resulting in noticeable image degradation. We use formulas developed for a two Hot Pixel interaction to estimate the probability of a Hot Pixel-SEU interaction, and predict its increase with camera age.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".