Invariant Image Improvement by sRGB colour space sharpening
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
Reasoning from image formation, we have shown that there exists a greyscale image – the invariant image – that depends only on the reflectances in the scene. Since illumination dependence is removed, one aspect of the invariant image is that shadows are effectively removed. Moreover, given either a calibration, or clean data with good noise statistics, this invariant is easily found. However, we found that the performance was much poorer on ordinary images that include the typical nonlinear processing in cameras. The contribution of this paper is that we can find a good invariant notwithstanding input image nonlinearities. Our strategy is to follow standard colorimetric procedure and convert image RGBs to the appropriate colour space for our method. We do this by converting first to the linear sRGB colour space and then concatenating conversion to XYZ tristimulus values by a spectral sharpening transform. We handle a suite of images which were intractable to the original method and are now able to find a shadow-free intrinsic reflectance image.
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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 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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