Zoro828/imageRestoration

🖼️ Image Restoration Studio

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    ![Status]()

Professional-grade image restoration with multi-algorithm inpainting

• Report Bug • Request Feature

<img src="https://github.com/FarazKhanAI/ImageRestoration/blob/main/demo.gif?raw=true" width="600" alt="Image Restoration Demo">

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🎯 What It Does

Restore damaged photos with AI-powered precision. Remove scratches, blemishes, and unwanted objects while preserving original details and colors.

✨ Key Features

• —✅ Multi-Algorithm Inpainting - Combines Telea, Navier-Stokes, and hybrid methods
• —✅ Smart Mask Processing - Feathering and edge-aware mask creation
• —✅ Color Preservation - Maintains original color consistency
• —✅ Fast Processing - 2-10 seconds for most images
• —✅ Batch Processing - Automatically saves masks and results

🚀 Get Started in 2 Minutes

Prerequisites

• —Python 3.10 or higher
• —Git (optional)

Installation

# 1. Clone the repository
git clone https://github.com/FarazKhanAI/ImageRestoration.git
cd ImageRestoration

# 2. Create virtual environment (optional but recommended)
python -m venv venv

# 3. Activate environment
# Windows:
venv\Scripts\activate
# Linux/Mac:
source venv/bin/activate

# 4. Install dependencies
pip install -r requirements.txt

# 5. Run the application
python app.py

Open your browser and visit: http://localhost:5000

📖 How to Use

Step 1: Upload Image

• —Drag & drop your image or click to browse
• —Supports JPG, PNG, BMP, TIFF (up to 16MB)

Step 2: Mark Damage Areas

• —Use the brush tool to mark scratches, stains, or objects
• —Adjust brush size with the slider
• —Use Undo/Clear if needed

Step 3: Adjust Parameters

• —Inpainting Method:
• —Fast (Telea) - Quick restoration
• —Quality (Navier-Stokes) - Better for textures
• —Hybrid - Best overall (recommended)
• —Brush Size: 5-100px
• —Inpainting Radius: 1-20px

Step 4: Process & Download

• —Click "Process & Restore Image"
• —View before/after comparison
• —Download restored image in high quality

🏗️ Technical Architecture

Backend Stack

Processing Pipeline

1. Image Upload → Validation → Resize
2. Mask Creation → Feathering → Dilation
3. Inpainting Selection → Algorithm execution
4. Color Correction → Post-processing
5. Result Generation → Metrics calculation

📁 Project Structure

image-restoration-app/
├── app.py                    # Main Flask application
├── config.py                # Configuration settings
├── requirements.txt         # Python dependencies
│
├── backend/                 # Core processing logic
│   ├── image_processor.py   # Main processing pipeline
│   ├── scratch_removal.py   # Advanced inpainting algorithms
│   ├── utils.py            # Helper functions (mask creation, I/O)
│   ├── enhancement.py      # Color adjustments (optional)
│   └── validators.py       # Input validation
│
├── templates/               # Frontend HTML
│   ├── base.html           # Base layout
│   └── index.html          # Main interface
│
├── static/                  # Web assets
│   ├── css/style.css       # Styling
│   └── js/main.js          # Interactive features
│
└── instance/               # User data (not in git)
    ├── uploads/raw/        # Original uploaded images
    ├── uploads/masks/      # Generated mask images
    └── processed/          # Restored output images

🧠 Advanced Inpainting Algorithms

Multi-Algorithm Strategy

The system intelligently selects the best algorithm based on damage size:

Key Technical Improvements

1. 1.Edge Preservation - Uses Canny edge detection to protect boundaries
2. 2.Color Matching - Adjusts inpainted colors to match surroundings
3. 3.Multi-Scale Processing - Handles different damage sizes optimally
4. 4.Soft Mask Blending - Feathering prevents visible seams

⚡ Performance & Optimization

Processing Times

Memory Usage

• —Minimal footprint: ~100MB for typical images
• —Auto-resizing: Large images automatically scaled to 2000px max dimension
• —Efficient cleanup: Temporary files removed after processing

🚢 Deployment Options

Option 1: Local Development

python app.py
# Runs on http://localhost:5000 with debug mode

Option 2: Production with Gunicorn

pip install gunicorn
gunicorn -w 4 -b 0.0.0.0:5000 app:app

Option 3: Free Cloud Deployment

Render.com (Recommended)

1. 1.Connect your GitHub repository
2. 2.Set build command: pip install -r requirements.txt
3. 3.Set start command: python app.py
4. 4.Get free HTTPS and auto-deploy

PythonAnywhere

1. 1.Upload files via web interface
2. 2.Configure WSGI file
3. 3.Free tier available (limited)

🔧 Configuration

Environment Variables

Create .env file for production:

SECRET_KEY=your-secret-key-here
MAX_CONTENT_LENGTH=16777216  # 16MB
DEBUG=False

Directory Setup

The application automatically creates:

instance/              # Created automatically
├── uploads/raw/      # Original images
├── uploads/masks/    # Mask images (saved here!)
└── processed/        # Final results

🐛 Troubleshooting

Common Issues & Solutions

Debug Mode

Enable debug logging by setting DEBUG = True in app.py:

# In app.py
DEBUG = True

🤝 Contributing

We welcome contributions! Here's how to help:

1. 1.Fork the repository
2. 2.Create a feature branch (git checkout -b feature/improvement)
3. 3.Commit your changes (git commit -m 'Add some improvement')
4. 4.Push to the branch (git push origin feature/improvement)
5. 5.Open a Pull Request

Development Setup

# Clone and setup
git clone https://github.com/FarazKhanAI/ImageRestoration.git
cd ImageRestoration
pip install -r requirements.txt

# Run tests
python -m pytest tests/

# Format code
black .

📄 License

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

📧 Support & Contact

• —GitHub Issues: Report bugs or request features
• —Email: [Your email or project email]
• —Project Link: https://github.com/FarazKhanAI/ImageRestoration

🙏 Acknowledgments

Special thanks to:

• —OpenCV Team - For the incredible computer vision library
• —Flask Community - For the lightweight web framework
• —All Contributors - Who help improve this project

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Ready to restore your images?

Get Started Now | View Demo

Made with ❤️ using Flask & OpenCV

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📚 Next Steps

Planned Improvements

• —[ ] Batch Processing - Restore multiple images at once
• —[ ] AI Enhancement - Add deep learning models for better results
• —[ ] Mobile App - iOS/Android companion apps
• —[ ] Cloud Storage - Google Drive/Dropbox integration

Frontend Updates (Coming Soon)

• —Dark mode toggle
• —More brush styles and shapes
• —Real-time preview while drawing
• —Advanced comparison slider

Tip: For best results, use high-quality source images and mark damage areas precisely with appropriate brush size.