Retinex-Based Multiphase Algorithm for Low-Light Image Enhancement
Why this work is in the frame
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Bibliographic record
Abstract
These days, digital images are one of the most profound methods used to represent information. Still, various images are obtained with a low-light effect due to numerous unavoidable reasons. It may be problematic for humans and computer-related applications to perceive and extract valuable information from such images properly. Hence, the observed quality of low-light images should be ameliorated for improved analysis, understanding, and interpretation. Currently, the enhancement of low-light images is a challenging task since various factors, including brightness, contrast, and colors should be considered effectively to produce results with adequate quality. Therefore, a retinex-based multiphase algorithm is developed in this study, in that it computes the illumination image somewhat similar to the single-scale retinex algorithm, takes the logs of both the original and the illumination images, subtract them using a modified approach, the result is then processed by a gamma-corrected sigmoid function and further processed by a normalization function to produce to the final result. The proposed algorithm is tested using natural low-light images, evaluated using specialized metrics, and compared with eight different sophisticated methods. The attained experiential outcomes revealed that the proposed algorithm has delivered the best performances concerning processing speed, perceived quality, and evaluation metrics.
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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 | 0.001 |
| 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