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An Image Enhancement Algorithm Based on Fractional-Order Phase Stretch Transform and Relative Total Variation.
Wei Wang1, Ying Jia2, Qiming Wang1
1School of Information Engineering, Pingdingshan University, Pingdingshan, Henan, China.
Computational Intelligence and Neuroscience
|January 29, 2021
Summary
This study introduces a novel fractional-order Phase Stretch Transform and relative total variation (FOPSTRTV) algorithm for superior image enhancement. The FOPSTRTV method effectively improves image detail and contrast, overcoming limitations of existing techniques.
Area of Science:
- Image Processing
- Computer Vision
- Signal Processing
Background:
- Image quality is crucial for applications in healthcare, industry, education, and surveillance.
- Complex environments often lead to images with insufficient detail and low contrast.
- Existing image enhancement algorithms struggle with overexposure and improper detail preservation.
Purpose of the Study:
- To enhance the treatment effect of Phase Stretch Transform (PST) on low and medium image frequencies.
- To develop a novel image enhancement algorithm addressing limitations of current methods.
- To improve image detail and contrast for better real-world applications.
Main Methods:
- A fractional-order Phase Stretch Transform and relative total variation (FOPSTRTV) algorithm was developed.
- Low-pass filtering was employed for noise reduction in original images.
- Fractional-order PST was used for edge extraction, followed by RTV-based fusion with the original image.
Main Results:
- The proposed FOPSTRTV algorithm demonstrated effective noise removal.
- Enhanced edge extraction and fusion improved image detail and contrast.
- Extensive experiments validated the algorithm's performance across diverse datasets.
Conclusions:
- The FOPSTRTV algorithm offers a significant improvement over existing image enhancement techniques.
- This method effectively addresses issues of insufficient detail and low contrast in challenging images.
- The enhanced image quality facilitates better performance in dependent applications like healthcare and surveillance.
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