Glare Pattern Depiction: High-Fidelity Physical Computation and Physiologically-Inspired Visual Response
Bibliographic record
Abstract
When observing an intense light source, humans perceive dense radiating spikes known as glare/starburst patterns. These patterns are frequently used in computer graphics applications to enhance the perception of brightness (e.g., in games and films). Previous works have computed the physical energy distribution of glare patterns under daytime conditions using approximations like Fresnel diffraction. These techniques are capable of producing visually believable results, particularly when the pupil remains small. However, they are insufficient under nighttime conditions, when the pupil is significantly dilated and the assumptions behind the approximations no longer hold. To address this, we employ the Rayleigh-Sommerfeld diffraction solution, from which Fresnel diffraction is derived as an approximation, as our baseline reference. In pursuit of performance and visual quality, we also employ Ochoa's approximation and the Chirp Z transform to efficiently generate high-resolution results for computer graphics applications. By also taking into account background illumination and certain physiological characteristics of the human photoreceptor cells, particularly the visual threshold of light stimulus, we propose a framework capable of producing plausible visual depictions of glare patterns for both daytime and nighttime scenes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".