Contrast Enhancement of Digital Images Using an Improved Type-II Fuzzy Set-Based Algorithm
Bibliographic record
Abstract
Contrast is a distinctive image feature that tells if it has adequate visual quality or not. On many occasions, images are captured with low-contrast due to inevitable obstacles. Therefore, an improved type-II fuzzy set-based algorithm is developed to enhance the contrast of various color and grayscale images properly while preserving the brightness and providing natural colors. The proposed algorithm utilizes new upper and lower ranges, amended Hamacher t-conorm, and a transform-based gamma correction method to provide the enhanced images. The proposed algorithm is assessed with artificial and real contrast distorted images, compared with twelve specialized methods, and the outcomes are evaluated using four advanced metrics. From the obtained results of experiments and comparisons, the developed algorithm demonstrated the ability to process various color and grayscale images, performed the best among the comparative methods, and scored the best in all four quality evaluation metrics. The findings of this study are significant because the proposed algorithm has low-complexity and can adjust the contrast of different images expeditiously, which enables it to be used with different imaging modalities especially those with limited hardware resources or produce high-resolution images.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".