Practical Scene Illuminant Estimation via Flash/No-Flash Pairs
Bibliographic record
Abstract
In this paper, we present a method to estimate ambient illuminants using no-flash/flash image pairs. Accurate estimation of the ambient illuminant is useful for imaging applications. In most applications, however, this task is difficult because of the complicated combination of illuminants, surfaces, and camera characteristics during the imaging process. To estimate the scene illumination, a version of the “illuminating illumination” method suggested by Dicarlo et al. is used. The method introduces camera flash light into the scene, and the reflected light is used to estimate the ambient illuminant. The original method needs an extra step of estimating the object surface reflectance, using a 3-dimensional linear surface model and the knowledge of the spectral responsivities of camera sensors. Here we consider the problem of estimating the ambient illuminant directly, with only flash/no-flash pairs, without information on surface reflectance and camera sensors. First, the flash image is registered with the no-flash image: the difference between the two gives a pure-flash image, as if it were taken under flash only. The no-flash and pure-flash images are represented by a physically-based model of image formation which uses assumptions of Lambertian surfaces, Planckian lights, and narrowband camera sensors. We argue that first going to a “spectrally sharpened” color space, and then projecting the difference in a log domain of the pure-flash image and the no-flash image into a geometric-mean chromaticity space, gives the chromaticity of the ambient illuminant. We verify that the chromaticities corresponding to illuminants with different temperatures fall along a line on a plane in the log geometric-mean chromaticity space. Simply by taking the nearest color temperature along this illuminant line, or classifying into one of potential illuminants, our algorithm arrives at an estimate of the illuminant.Remarkably, our algorithm is truly practical as it can estimate the color of the ambient light even without any prior knowledge about surface reflectance, flash light, or camera sensors. Experiments on real images demonstrate that estimation accuracy can be very good.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".