Colour Image Gradient Regression Reintegration
Bibliographic record
Abstract
Suppose we process an image and alter the image gradients in each colour channel R,G,B. Typically the two new x and y component fields p,q will be only an approximation of a gradient and hence will be nonintegrable. Thus one is faced with the problem of reintegrating the resulting pair back to image, rather than derivative of image, values. This can be done in a variety of ways, usually involving some form of Poisson solver. Here, in the case of image sequences or video, we introduce a new method of reintegration, based on regression from gradients of log-images. The strength of this idea is that not only are Poisson reintegration artifacts eliminated, but also we can carry out the regression applied to only thumbnail images. The novel approach here is to regress derivatives (using only thumbnails) and then replace reintegration itself by the much simpler use of the resulting regression coefficients on non-derivative, full-size images. We investigate the utility of the method by applying it to the intrinsic-image problem as a first test, and then also to the night-to-day problem as a second test. We find that the new algorithm performs well, and is fast. Moreover eliminating Poisson artifacts results in clearer, more sharp output images that can show far less ghosting.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".