Background- and Ambient-aware Image Visibility Enhancement for Transparent Displays
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
The transparent display has attracted attention from industrial and research fields due to its broad applications in many fields, including mobile displays, wearable devices, and head-up displays in automobiles. However, the visibility of images on transparent displays is degraded under bright ambient conditions and cluttered backgrounds. This paper analyzes visibility degradation under various backgrounds and ambient conditions. Then, a background- and ambient-aware visibility enhancement algorithm is proposed, where the transmittance rejection rate for degraded pixels is adjusted to enhance the visibility. The experimental results show that the proposed algorithm can enhance visibility under various backgrounds and ambient conditions, which promises the applicability of a transparent display in outdoor environments.
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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 | 0.000 | 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