Color Constancy for Multiple-Illuminant Scenes using Retinex and SVR
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
Scenes lit by multiple colors of illumination provide a problem for color constancy and automatic white balancing algorithms. Many of these algorithms estimate a single illuminant color, but since when there are multiple illuminants, there is in fact not a single correct answer. For automatic white balancing and color-cast removal in digital images, multiple illuminants mean that a single, image-wide adjustment of colors may not yield a good result, since the adjustment that makes one image area look better, may simultaneously make another look worse. Retinex is one method that adjusts colors on a pixel-by-pixel basis, and so inherently addresses the multiple-illumination problem, but it does not always produce a perfect overall color balance. On the other hand, illumination estimation by Support Vector Regression (SVR), produces quite good overall color balance for single-illuminant scenes, but does not adjust the colors locally. By combining Retinex and SVR in to a hybrid Retinex+SVR method, some of these problems can be overcome. Experiments with both synthetic and real images show promising results.
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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