An improved Bayesian algorithm for color image desaturation
Bibliographic record
Abstract
Current digital imaging systems are unable to capture the entire dynamic range of the visible luminance, causing saturation in the very bright parts of a scene. Color distortion occurs when the amounts of saturation are different in the red (R), green (G), and blue (B) color channels. A Bayesian algorithm was developed in the past to correct the saturated pixels in raw images. For each image, it estimates the distributions of the R, G, and B color channels based on the unsaturated pixels, and then corrects the saturated pixels based on this prior distribution. In this paper, we improve this Bayesian algorithm by incorporating spatial information in the correction process. We utilize the strong spatial correlation of images as well as the correlation between the R, G, and B channels of each individual pixel to estimate the prior distributions of the R, G, and B color channels. The prior distribution of each saturated region is modeled individually based on its surrounding region, which is determined by morphological dilation. Experimental results show that our modified algorithm greatly outperforms the original Bayesian algorithm for fixing saturated pixels in color images.
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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 | 0.001 |
| Open science | 0.001 | 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".