The Evaluation of Nature-Inspired Optimization Techniques for Contrast Enhancement in Images: A Novel Software Tool
Bibliographic record
Abstract
This study is rooted in the direct correlation between the performance of multivariate techniques and the selection of parameters.The complexity and time-consuming nature of parameter selection, due to the need for exhaustive testing of all available parameters for optimal results, is acknowledged.To mitigate this issue, a novel software tool, integrating nine nature-inspired optimization methods (Differential Evolution, Artificial Bee Colony, Particle Swarm, Cat Swarm, Dragonfly, Black Hole, Bacterial Foraging, Genetic Algorithms, and Simulated Annealing), is proposed.These methods are employed in histogram stretching, a parameter-dependent contrast enhancement technique, with multiplication, addition, and root extraction operations as the target parameters for optimization.In addition to this, histogram equalization, a parameter-independent contrast enhancement technique, is included for the purpose of comparative performance analysis.The software tool, publicly available, provides four performance metrics namely, Mean Square Error, Peak Signal-to-noise Ratio, Structural Similarity Index, and processing times.A rigorous evaluation using the widely recognized Tampere Image dataset indicates that Differential Evolution emerged as the most efficient technique, scoring highest for Structural Similarity Index (0.948) and second best for Mean Square Error (278.05) and Peak Signal to Noise Ratio (26.962).Furthermore, Particle Swarm Optimization demonstrated the fastest time complexity, requiring merely 0.6 sec per image for parameter definition.Notably, it was observed that while histogram equalization tends to degrade original images, the adaptive nature of optimized histogram stretching remains preserved, thereby leaving the image quality unaffected.Such findings highlight the efficacy of the proposed software tool in the optimization and evaluation of contrast enhancement techniques.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".