A fusion-based method for single backlit image enhancement
Why this work is in the frame
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Bibliographic record
Abstract
In this work, a new simple but effective fusion-based strategy for enhancing single backlit image is proposed. The fundamental idea of proposed strategy is to blend different features into a single one to improve the specific quality of image. Most of existing methods are based on the modification of histogram to enhance the contrast of low light images. However, the backlit images are different from low light images, which have wide dynamic ranges of light regions, thus the existing methods cannot achieve good enhanced results of backlit images. To improve performance of enhanced results, the proposed method considers numerous features of images and processes the dark and bright regions, respectively. Furthermore, proposed method introduces weight maps to increase the visibility. Experimental results show that proposed method is superior to existing methods, which achieves better results both in visual effects and processing time.
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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 | 0.001 | 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 | 0.001 | 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