Contrast enhancement by adaptive mapping function with local information
Bibliographic record
Abstract
Real scenes usually have a wider dynamic range than that of image acquisition devices, which makes most of the image acquired in under/over exposed conditions. Although the existing contrast enhancement algorithms such as contrast limited adaptive histogram equalization (CLAHE) are capable of restoring some of the details degraded in the acquisition, they may fail to detect others. This is mainly due to the complex degradation cases. It is necessary that any new mapping function should be modulated according to the local information. On this line, a new algorithm based on the CLAHE is proposed in this paper. The image quality improvement is focused on the enhancement of the local contrast in an image, in which the signal degradation could result in homogeneous sections in its histogram. It is achieved by discriminative clipping of the pixels based on its intensity levels and redistributing the pixels to maximize the available dynamic range. The contrast enhancement is performed locally based on the information of the local pixels. The clipped pixels are redistributed more assertively near histogram peaks to improve the image quality subjectively with maximum local contrast enhancement. The simulation result shows that the proposed method significantly enhances the image quality.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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 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".