Histogram Shape Based Gaussian Histogram Specification for Contrast Enhancement
Why this work is in the frame
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Bibliographic record
Abstract
Contrast enhancement a critical component in image processing is a vital integral part of computer vision in all fields of engineering including surveillance, medical, agricultural, aerospace, electrical, mechanical, etc. Although the existing contrast enhancement methods achieved satisfactory enhancement, they can produce annoying side effects due to variation of intensity levels. In this article, a new model for contrast enhancement that makes use of the given image's histogram shape to capture the variation in the intensity distribution to avoid annoying side effects is anticipated. In the proposed Histogram shape based Gaussian Histogram Specification technique, the desired histogram is obtained by dynamically controlling the parameters, mean and standard deviation. Using the images taken from standard and NASA database along with quality metrics such as contrast, entropy and gradient, the proposed Gaussian Histogram Specification technique performed better than that of the existing contrast enhancement techniques.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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