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Adaptive low-light image enhancement using Interval-Valued Intuitionistic Fuzzy Set optimized by Reptile Search
Haripriya Yogambaram1, M Sivabalakrishnan2, S Balaji1
1Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, India.
Frontiers in Artificial Intelligence
|January 28, 2026
Summary
This study introduces an enhanced low-light image enhancement model using Interval-Valued Intuitionistic Fuzzy Set and Reptile Search Algorithm. The method significantly improves image clarity, brightness, and structural detail for medical and autonomous systems.
Area of Science:
- Computer Vision
- Image Processing
- Artificial Intelligence
Background:
- Low-light imaging presents challenges in balancing brightness and natural appearance.
- Medical imaging and autonomous systems require high-quality images in all lighting conditions.
Purpose of the Study:
- To develop an advanced image enhancement model for low-light conditions.
- To improve image quality by optimizing brightness, contrast, and structural preservation.
Main Methods:
- A novel approach combining Interval-Valued Intuitionistic Fuzzy Set (IVIFs) with Reptile Search Algorithm (RSA) optimization.
- Automatic tuning of fuzzy membership and hesitation factors to handle uncertainty in dark areas.
- Evaluation using objective metrics: Peak Signal-to-Noise Ratio (PSNR), Absolute Mean Brightness Error (AMBE), Contrast Improvement Index (CII), and entropy.
Main Results:
- Achieved a 3.69% gain in entropy.
- Demonstrated a 21.71% improvement in brightness restoration.
- Reported an 18.73% gain in contrast.
- Showcased a significant 66.12% increase in PSNR compared to the baseline IVIFs method.
Conclusions:
- The proposed technique effectively enhances low-light images, yielding natural-looking results with improved clarity and structural integrity.
- The method is highly applicable to real-world scenarios requiring superior low-light image quality, such as in medical diagnostics and autonomous navigation.
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