Whale Optimization Algorithm for Enhancing Grayscale Images
Author Information
Author(s): Han Yibo, Hu Pei, Su Zihan, Liu Lu, Panneerselvam John, Jia Heming, Abualigah Laith, Xia Xuewen
Primary Institution: School of Computer and Software, Nanyang Institute of Technology, Nanyang, China
Hypothesis
Can the whale optimization algorithm improve the enhancement of grayscale images?
Conclusion
The advanced whale optimization algorithm significantly enhances grayscale images, outperforming traditional methods in various metrics.
Supporting Evidence
- The algorithm outperformed comparison algorithms in 11 out of 15 test cases.
- AWOA showed superior performance in PSNR, FSIM, SSIM, and PCQI metrics.
- The Wilcoxon rank sum test confirmed AWOA's best overall performance.
- AWOA maintained image details while enhancing contrast effectively.
Takeaway
This study shows that a special algorithm inspired by whale behavior can make black-and-white pictures look much better.
Methodology
The study used a transformation function optimized by the whale optimization algorithm to enhance grayscale images.
Limitations
The algorithm's effectiveness on color and high-resolution images is not fully explored.
Statistical Information
P-Value
0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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