IMPROVEMENT OF COLORIZATION REALISM VIA THE STRUCTURE TENSOR
Why this work is in the frame
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Bibliographic record
Abstract
Colorization is a color manipulation mechanism employing user-assisted color hints for changing grayscale images into colored ones. Several colorization algorithms have been constructed, and many of these methods are able to produce appropriately colorized images given a surprisingly sparse set of hints supplied by the user. However, these color images may not in fact look realistic. Moreover, the contrast in the colorized image may not match the gradient perceived in the original grayscale image. We argue that it is this departure from the original gradient that contributes to the unreal appearance in some colorizations. To correct this, we make use of the Di Zenzo gradient of a color image derived from the structure tensor, and adjust the colorized image such that the Di Zenzo definition of the maximum-contrast gradient agrees with the gradient in the original gray image. We present a heuristic method to this end and guided by this approach devise an optimization-based method. Our gradient projection tends to result in more natural-looking images in the resulting adjusted colorization. To explore the proposed method we utilize minimalist sets of color hints and find in particular that "hotspots" of unrealistic color are subdued into regions of more realistic color. This paper is not aimed at introducing a new basic colorization but instead our method is meant to make any colorization look more realistic; we demonstrate that this is the case for several different basic methods. In fact, we even find that a very simplistic colorization algorithm can be used provided the projection proposed here is then used to make the colorization more realistic looking.
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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 | 0.001 | 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 it