Kajian Algoritma Peningkatan Kontras Citra Dengan Fast Hue Dan Range Preserving Histogram Equalization Specification
Bibliographic record
Abstract
Faktor pencahayaan yang kurang saat suatu citra diakuisisi membuat citra menjadi gelap. Untuk memperbaiki tingkat kecerahan kontras citra, beberapa metode telah dilakukan seperti Fast Hue and Range Preserving Histogram Equalization Specification yang meliputi Algoritma Naik and Murthy, algoritma Optimal Range-Preserving Enhancement, algoritma Multiplicative Color Enhancement dan algoritma Additive Color Enhancement. Pada tahap awal dilakukan proses perataan histogram (Histogram Equalization (HE)). Namun dari beberapa referensi belum dapat ditentukan algoritma yang lebih baik dalam proses peningkatan kontras tersebut. Skenario pengujian dilakukan dengan menurunkan nilai lightness dari suatu citra, memproses citra gelap dengan algoritma yang dibahas, dan mengukur perbedaan citra hasil algoritma dengan citra asli menggunakan Structural Similarity Index (SSIM). ??? Hasil pengujian menunjukkan bahwa ??? nilai SSIM tertinggi didapatkan dengan menggunakan algoritma Optimal Range-Preserving Enhancement dan algoritma Multiplicative Color Enhancement. Pada algoritma Optimal Range-Preserving Algorithm, nilai SSIM tertinggi diperoleh dengan menggunakan nilai Lamda (???») di atas 0.6.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".