Preserving Brightness While Enhancing Visibility with Fuzzy Logic
In medical imaging, surveillance, and satellite imagery, low-contrast images often limit visibility and interpretation. Conventional enhancement methods can over-amplify noise or distort brightness.
This project introduces a Fuzzy Intensification-Based Enhancement Algorithm that:
✔ Boosts visibility while preserving brightness
✔ Handles uncertainty with Intuitionistic Fuzzy Sets (IFS)
✔ Enhances image contrast without excessive noise amplification
🔍 Used in: Medical diagnostics, security surveillance, satellite imaging, and low-light photography.
- 🖼️ Adaptive Image Enhancement – Uses fuzzy logic and histogram equalization to enhance low-contrast images.
- 🤖 Intuitionistic Fuzzy Sets (IFS) – Overcomes uncertainty by considering membership, non-membership, and hesitation degrees.
- ⚡ Real-Time Processing – Optimized entropy calculations for adaptive image enhancement.
- 📊 Quantitative Evaluation – Achieves higher SSIM and lower brightness error than traditional methods.
1️⃣ Preprocessing: Uses Non-Local Means Filtering (NLMF) to reduce noise.
2️⃣ Fuzzification: Converts pixel intensities into fuzzy sets to handle uncertainties.
3️⃣ Entropy-Based Optimization: Adjusts parameters dynamically to maximize image clarity.
4️⃣ Fusion & Enhancement: Intuitionistic Fuzzy logic is applied to integrate multiple modalities (e.g., CT & MRI).
5️⃣ Quantitative Analysis: Evaluates image enhancement with SSIM, AMBE, Entropy, and FSIM.
| Image | AMBE (↓) | Entropy (↑) | SSIM (↑) | FSIM (↑) | GMSD (↓) |
|---|---|---|---|---|---|
| 1.jpg | 12.49 | 7.99 | 0.80 | 0.85 | 0.13 |
| 2.jpg | 12.04 | 7.95 | 0.63 | 0.70 | 0.18 |
| 3.jpg | 36.38 | 7.67 | 0.38 | 0.43 | 0.17 |
✅ Python 🐍
✅ OpenCV & NumPy 🖼️
✅ Matplotlib & Seaborn 📊
✅ Fuzzy Logic & Intuitionistic Fuzzy Sets 🤖
Clone the repository and run the enhancement script:
git clone https://github.com/YOUR_USERNAME/fuzzy-enhancement.git
cd fuzzy-enhancement
python enhance.py --input image.jpg --output enhanced.jpg