Fast and Efficient Algorithm for Contrast Enhancement of Color Images
Bibliographic record
Abstract
The contrast is one image feature that when reduced, it diminishes the visibility of important details in an image.Many captured images are distorted by a low contrast effect, in that this effect should be handled properly to increase the perceived quality of such degraded images.In this study, a simple contrast enhancement algorithm is proposed, in that it comprises of five different stages.The first stage includes the application of a hyperbolic sine function to provide a simple transformation of image contrast, while the second stage includes the application of a modified power-law function to manage the contrast adjustment.The third stage includes the use of a standard sigmoid function to reallocate the intensities in an "S" shape form, which can provide further enhancement, while a cumulative distribution function of Gompertz distribution is used as the fourth processing stage to improve the image brightness.In the ending stage, a contrast stretching function is applied to redistribute the intensities to the standard interval.Rigorous tests proved that the developed algorithm can well-process low contrast color images and can outperform other available methods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".