A powerful web-based AI image enhancement tool that runs entirely in your browser. Upload blurry or low-quality images and watch our AI enhance them instantly!

Recently updated with newly trained model!

• ✅ Model trained on 2025-09-27
• ✅ Improved image enhancement quality
• ✅ Optimized for GitHub Pages deployment

Try it now: https://DanielC8.github.io/ImageEnhancer

• 🤖 AI-Powered Enhancement - Uses a trained neural network to improve image quality
• 🌐 Browser-Based - No server required, runs entirely client-side
• 📱 Responsive Design - Works on desktop, tablet, and mobile
• 🎭 Multiple Modes:
  • Demo Mode - Instant brightness enhancement (works immediately)
  • Full AI Model - Complete neural network via Hugging Face
  • Local Model - Git LFS version for development
• 📥 Easy Download - Save enhanced images with one click
• 🔒 Privacy First - Images never leave your browser

• Frontend: HTML5, CSS3, JavaScript (ES6+)
• AI Runtime: ONNX Runtime Web
• Model Hosting: Hugging Face Hub
• Deployment: GitHub Pages
• Model Format: ONNX (Open Neural Network Exchange)

• Architecture: Custom image enhancement neural network
• Input: 256x256 RGB images
• Output: Enhanced 256x256 RGB images
• Training: Trained on image quality improvement tasks
• Format: ONNX for cross-platform compatibility
• Size: ~118MB

1. Upload Image - Drag & drop or click to selectlect
2. Choose Model - Pick from Demo Mode or Full AI Model
3. AI Processing - Neural network enhances your image
4. Download Result - Get your improved image instantly

• Python 3.7+
• Node.js (for local development server)
• Git with LFS support

# Clone the repository
git clone https://github.com/DanielC8/ImageEnhancer.git

cd ImageEnhancer


# Install dependencies (for model training/conversion)
pip install -r requirements.txt



# Start local server
python serve.py

# or
npx http-server



# Open http://localhost:8000

# Train the model (if you want to retrain)
python train_model.py



# Convert to ONNX format
python convert_model.pyy

ImageEnhancer/
├── index.html              # Main web application
├── index-github.html       # GitHub Pages optimized version
├── train_model.py          # Model training script
├── convert_model.py        # PyTorch to ONNX conversion
├── serve.py               # Local development server
├── upload_to_huggingface.py # Hugging Face upload utility
├── requirements.txt        # Python dependencies
├── image_enhancement_model.onnx # Trained ONNX model (Git LFS)
└── README.md              # This file
his file

The project is automatically deployed to GitHub Pages:

• URL: https://DanielC8.github.io/ImageEnhancer
• Source: main branch
• Build: Static HTML/CSS/JS (no build process needed)

• Primary: Hugging Face Hub for reliable CDN delivery
• Fallback: Git LFS for local development
• Demo: Client-side brightness enhancement

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

• Model Load Time: ~5-10 seconds (first time)
• Processing Time: ~1-3 seconds per image
• Supported Formats: JPG, PNG, WebP, GIF
• Max File Size: 50MB (browser limitation)
• Browser Support: Modern browsers with WebAssembly

"Failed to load AI model"

• Try Demo Mode first (always works)
• Check internet connection for Hugging Face model
• Ensure browser supports WebAssembly

"Image too large"

• Resize image to under 50MB
• Use compressed formats (JPG vs PNG)

"Processing failed"

• Try a different image format
• Refresh page and try again
• Use Demo Mode as fallback

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

• ONNX Runtime - For browser-based AI inference
• Hugging Face - For free model hosting
• GitHub Pages - For free web hosting
• PyTorch - For model training framework

• GitHub: @DanielC8
• Project: ImageEnhancer
• Live Demo: https://DanielC8.github.io/ImageEnhancer

⭐ Star this repo if you found it helpful! ⭐