Invariant Image Improvement by sRGB colour space sharpening
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".