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An optimal fuzzy system for color image enhancement
Madasu Hanmandlu1, Devendra Jha
1Indian Institute of Technology, Delhi, New Delhi, India. mhmandlu@ee.iitd.ac.in
Summary
This study introduces a novel method using a Gaussian membership function and a global contrast intensification operator (GINT) to enhance color image quality. The technique effectively improves under-exposed images by reducing entropy, with parameters optimized for visual and quality factors.
Area of Science:
- Digital Image Processing
- Computer Vision
- Computational Intelligence
Background:
- Image enhancement is crucial for visual interpretation and analysis.
- Existing methods may struggle with varying illumination conditions, particularly under- and over-exposed images.
- Fuzzy logic offers a powerful framework for handling image information uncertainty.
Purpose of the Study:
- To propose a new image enhancement technique for color images using fuzzy logic.
- To introduce a global contrast intensification operator (GINT) for image enhancement.
- To develop methods for optimizing enhancement parameters based on image quality and visual factors.
Main Methods:
- Utilized a Gaussian membership function to fuzzify spatial image information.
- Introduced the global contrast intensification operator (GINT) with parameters: intensification parameter (t), fuzzifier (fh), and crossover point (μc).
- Defined fuzzy contrast-based quality factor (Qf) and entropy-based quality factor (Qe) to guide parameter optimization.
- Minimized fuzzy entropy with respect to quality factors to globally calculate GINT parameters.
- Modified the fuzzification function for over-exposed images by incorporating maximum intensity.
Main Results:
- Demonstrated visible improvement in image quality, particularly for under-exposed images, evidenced by decreased output image entropy.
- Achieved parameter calculation (t, fh, μc) globally by minimizing fuzzy entropy.
- Established a visual factor to differentiate between under-exposed (<1) and over-exposed (>1) images.
Conclusions:
- The proposed Gaussian-based fuzzification and GINT operator effectively enhance color images, especially under-exposed ones.
- The method provides a robust way to optimize enhancement parameters using quality and visual factors.
- Adaptability for over-exposed images is achieved through a modified fuzzification approach.
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