Adaptive hybrid mean and median filtering of high-ISO long-exposure sensor noise for digital photography
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a new methodology for the reduction of sensor noise from images acquired using digital cameras at high-International Organization for Standardization (ISO) and long-exposure settings. The problem lies in the fact that the algorithm must deal with hardware-related noise that affects certain color channels more than others and is thus nonuniform over all color channels. A new adaptive center-weighted hybrid mean and median filter is formulated and used within a novel optimal-size windowing framework to reduce the effects of two types of sensor noise, namely blue-channel noise and JPEG blocking artifacts, common in high-ISO digital camera images. A third type of digital camera noise that affects long-exposure images and causes a type of sensor noise commonly known as "stuck-pixel" noise is dealt with by preprocessing the image with a new stuck-pixel prefilter formulation. Experimental results are presented with an analysis of the performance of the various filters in comparison with other standard noise reduction filters.
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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 | 0.001 | 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 | 0.001 |
| 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