Enhancement of Images with Very Low Light by Using Modified Brightness Low Lightness Areas Algorithm Based on Sigmoid Function
Bibliographic record
Abstract
Enhancement of images with very low light has become an important role in the field of digital image processing, especially during night photography, tracking and medical imaging using binoculars. In this study, a new algorithm was proposed to enhance images with very low light on the basis of the development of brightness low lightness areas algorithm with the treatment of lighting component (Y) by using Sigmoid function in accordance with YIQ colour space. The proposed method was compared with several algorithms as (contrast enhancement approach, multi-scale retinax with color restoration, histogram equalization, fuzzy logic based-on sigmoid membership function, second-order Taylor series approximation and parallel nonlinear adaptive enhancement) by using non-reference quality measures on the basis of LIME data. Results showed the success of the proposed method on improving images with very low light, obtaining the best quality values rates of Entropy (6.81), NIQE (3.46) and PIQE (35.87).
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".