IFG-Based Contrast Enhancement

A standalone, cross-platform application for low-contrast color image enhancement using the Intuitionistic Fuzzy Generator (IFG) approach.

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Overview

This application provides an easy-to-use interface for enhancing low-contrast images using the Intuitionistic Fuzzy Generator (IFG)–based algorithm introduced by Selvam et al., 2024 (Information Fusion).

It combines the novel IFG method with Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve visibility and contrast in dark or low-illumination images. The project wraps this method in a Qt-based GUI for side-by-side visual comparison.

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Installation

From Source

Clone this repository and install dependencies:

git clone [email protected]:Adi8712/IFGContrastEnhancer.git
cd IFGContrastEnhancer
python -m venv .venv
source .venv/bin/activate       # or .venv\Scripts\activate on Windows
pip install -r requirements.txt

Run the app:

python main.py

From Executable

If you built it with PyInstaller:

./dist/IFGContrastEnhancer

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Usage

1. Open the application Launch IFGContrastEnhancer or run python main.py.

2. Load an image

   • The file dialog opens inside the bundled samples/ folder by default.
   • Supported formats: .png, .jpg, .jpeg, .bmp, .tiff.

3. Run Enhancement

   • Click Run Enhancement to process the image using both CLAHE and IFG methods.
   • The left pane shows the CLAHE-enhanced image.
   • The right pane shows the IFG-enhanced result.

4. Compare & Export

   • Drag the divider line to compare the two results interactively.
   • Use Save CLAHE or Save IFG to export individual outputs.

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Project Structure

.
├── main.py                  # GUI and application entrypoint
├── src/
│   ├── __init__.py
│   ├── clahe.py             # CLAHE enhancement module
│   └── ifg.py               # IFG-based enhancement algorithm
├── samples/                 # Sample images for testing
├── pyproject.toml           # Project configuration
├── requirements.txt         # Dependency list
└── README.md

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Dependencies

Core dependencies (see requirements.txt):

• Python ≥ 3.13
• PySide6 — Qt-based GUI framework
• opencv-python — Image processing
• numpy — Numerical computation
• matplotlib — Optional plotting utilities

Install all dependencies with:

pip install -r requirements.txt

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Packaging (Optional)

To build a standalone executable (using PyInstaller):

pyinstaller --onefile --name IFGContrastEnhancer --add-data "samples:samples" main.py

This will generate a single packaged binary inside the dist/ folder.

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Samples

Sample images used for testing and demonstration were sourced from: Google Drive – LOw Light paired dataset (LOL)

These images are provided for educational and evaluation purposes.

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Credits and Acknowledgments

This application is an open-source implementation of the image enhancement algorithm described in:

Selvam, C., Jebadass, R.J.J., Sundaram, D., & Shanmugam, L. A novel intuitionistic fuzzy generator for low-contrast color image enhancement technique. Information Fusion, 108 (2024), 102365. https://doi.org/10.1016/j.inffus.2024.102365

All mathematical formulations and algorithmic contributions belong to the original authors. This project provides an accessible, visual implementation for educational, research, and demonstration use.

Developed and adapted for general-purpose use by me.

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License

This project is distributed under the MIT License. You are free to use, modify, and distribute the software, provided proper credit is given to the original authors and contributors.

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