Simplifying Tone Curves for Image Enhancement
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
A single tone curve which is used to globally remap the brightness of each pixel in an image is one of the simplest ways to enhance an image. Tone curves might be the result of individual user edits or from algorithmic processing including in-camera processing pipelines. The precise shape of the tone curve is not strongly constrained other than it is usually limited to increasing functions of brightness. In this paper we constrain the shape further and define a simple tone adjustment, mathematically, to be a tone curve that has either no or one inflexion point. It follows that a complex tone curve is one with more than one inflexion point, visually making the curve appear ‘wiggly’. Empirically, complex tone curves do not seem to be used very often. For any given tone curve we show how the closest simple approximation can be efficiently found. We apply our approximation method to the MIT-Adobe FiveK dataset which comprises 5000 images that are manually tone-edited by 5 experts. For all 25,000 edited images - where some of the tone adjustments are complex - we find that they are all well-approximated by simple tone curve adjustments.
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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