Adaptive Contrast Enhancement

A high-performance web application for enhancing image contrast using adaptive algorithms with an emphasis on speed, usability, and mobile responsiveness.

Features

Core Functionality

• Upload and process images with intelligent compression
• Apply adaptive contrast enhancement with real-time feedback
• Fine-tune enhancement parameters with intuitive controls
• View before/after comparison in real-time
• Save and download enhanced images
• Track processing history with parameter reuse

Performance Optimizations

• Intelligent image compression to reduce processing time
• Asynchronous processing with background tasks
• Real-time progress tracking with estimated completion time
• Smart caching system to avoid redundant processing
• Memory management to prevent leaks during processing
• Adaptive algorithm selection based on image characteristics

User Experience

• Modern, intuitive interface with responsive design
• Mobile-friendly layout that works on all device sizes
• Real-time loading indicators with progress tracking
• Preset system for quick enhancement application
• Detailed history tracking with parameter reuse
• Performance insights for problematic images
• Modular Architecture: Well-structured codebase with separate modules for different functionalities

Installation

1. Clone this repository:

git clone https://github.com/yourusername/imageprocessing.git
cd imageprocessing

2. Install the required dependencies:

pip install -r requirements.txt

Usage

Development Mode

1. Run the application in development mode:

python main.py

2. The web interface will automatically open in your default browser at http://localhost:5000

3. Upload an image, adjust parameters, and apply the enhancement

4. Download the enhanced image

Production Mode

For production deployment, use the included production server (Waitress):

python production.py

This will start a production-ready WSGI server. You can specify host and port:

python production.py --host 0.0.0.0 --port 8080

Project Structure

imageprocessing/
├── filters/                  # Filter implementations
│   ├── adaptive_enhancement.py  # Core adaptive enhancement algorithm
│   ├── advanced_filters.py   # Advanced filter implementations
│   └── basic_filters.py      # Basic filter implementations
├── static/                   # Static web assets
│   ├── css/                  # CSS styles
│   ├── js/                   # JavaScript files
│   ├── img/                  # Static images
│   ├── uploads/              # User uploaded images
│   └── results/              # Enhanced images
├── templates/                # HTML templates
│   └── index.html            # Main page template
├── utils/                    # Utility functions
│   └── image_utils.py        # Image processing utilities
├── app.py                    # Flask application
├── main.py                   # Main entry point
└── requirements.txt          # Project dependencies

Parameters Guide

General Parameters

• Window Size: Size of the local window for calculating statistics (larger values = smoother results)
• Use Entropy: Whether to use local entropy for enhancement (helps preserve details in complex regions)

Noise Reduction Parameters

• Apply Noise Reduction: Enable/disable noise reduction
• Bilateral Strength: Strength of the bilateral filter (higher = more smoothing)
• Bilateral Diameter: Diameter of each pixel neighborhood for bilateral filter
• Denoise Blend Factor: How much of the denoised image to blend with the original

Contrast Enhancement Parameters

• Gamma Min: Minimum gamma value for adaptive gamma correction
• Gamma Max: Maximum gamma value for adaptive gamma correction
• Contrast Strength: Strength of local contrast enhancement
• Entropy Factor: How much local entropy influences enhancement

Detail Enhancement Parameters

• Enhance Details: Enable/disable detail enhancement
• Unsharp Kernel Size: Size of the kernel for unsharp masking
• Unsharp Sigma: Standard deviation of the Gaussian kernel for unsharp masking
• Unsharp Amount: Strength of the sharpening effect

Final Adjustment Parameters

• Apply CLAHE: Enable/disable Contrast Limited Adaptive Histogram Equalization
• CLAHE Clip Limit: Threshold for contrast limiting in CLAHE
• CLAHE Blend: How much of the CLAHE result to blend with the enhanced image
• Apply High Boost: Enable/disable high-boost filtering
• High Boost Factor: Factor to boost high frequencies
• High Boost Blend: How much of the high-boost result to blend with the enhanced image

Presets

• Dark Image: Optimized for very dark images, increases brightness and contrast
• Bright Image: Optimized for very bright images, reduces highlights and improves details
• Sharp Edges: Enhances edges while preserving details
• Blurry Image: Increases sharpness and detail for blurry images
• High Detail: Optimized for images with strong visible detail
• Low Contrast: Enhances contrast in flat, low-contrast images

Algorithm Details

Adaptive Enhancement Process

1. Local Statistics Calculation:

   • Calculate local mean, standard deviation, and entropy for each pixel
   • These statistics guide the adaptive enhancement process

2. Noise Reduction:

   • Apply bilateral filtering with adaptive parameters
   • Smooth flat regions more aggressively while preserving edges

3. Adaptive Enhancement:

   • Apply adaptive gamma correction based on local brightness
   • Enhance contrast based on local standard deviation
   • Use entropy to further adjust enhancement in complex regions

4. Detail Enhancement:

   • Apply unsharp masking with adaptive amount
   • Enhance details more in high-contrast regions

5. Final Adjustments:

   • Apply CLAHE for additional contrast enhancement
   • Use high-boost filtering to enhance high-frequency details

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• OpenCV for image processing functions
• Flask for the web framework
• Scikit-image for additional image processing algorithms