Color Constancy for Multiple-Illuminant Scenes using Retinex and SVR
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".