Kajian Algoritma Peningkatan Kontras Citra Dengan Fast Hue Dan Range Preserving Histogram Equalization Specification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it