NAFNet Image Restoration API

Enterprise-grade Image Restoration & Enhancement API

Restore, denoise, and enhance images with production-ready Flask API and modern web interface.

Quick Start • Installation • API Docs • Examples • Deployment • Contributing

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📋 Table of Contents

• Overview
• Features
• Quick Start
• Installation
• API Documentation
• Usage Examples
• Deployment
• Architecture
• Performance
• Development
• Contributing
• License
• Support

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Overview

NAFNet Image Restoration API is a professional-grade, production-ready REST API for image restoration and enhancement. Built with Flask and optimized for high performance, it combines advanced image processing techniques with a modern, intuitive web interface.

Use Cases

• Photo Restoration: Restore old or degraded photographs
• Noise Reduction: Remove noise from low-light or high-ISO images
• Quality Enhancement: Improve overall image clarity and sharpness
• Batch Processing: Process multiple images efficiently
• Integration: Embed in larger applications via REST API
• Enterprise Deployment: Run on Docker with health checks and monitoring

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✨ Features

🎨 Web Interface

• Drag-and-drop upload with validation
• Real-time preview showing before/after comparison
• Automatic download of processed images
• Responsive design for desktop, tablet, and mobile
• Modern UI with smooth animations and feedback

💻 RESTful API

• Single endpoint for restoration: POST /api/fix
• Info endpoint for API details: GET /api/info
• JSON error responses with descriptive messages
• Content negotiation and proper HTTP status codes
• File size limits (50MB) for security

🔧 Image Processing Pipeline

1. Denoise - Median filtering to remove noise while preserving edges
2. Smooth - Gaussian blur for artifact reduction
3. Enhance Contrast - 1.3x multiplier for visual impact
4. Sharpen - 1.2x multiplier for edge definition
5. Saturate Colors - 1.1x multiplier for color vibrancy

🚀 Production Ready

• Error handling with validation on all inputs
• Security - File size limits, input validation, safe file operations
• Performance - Optimized image processing, efficient memory usage
• Monitoring - Docker health checks, structured logging
• Documentation - Comprehensive API docs and examples

📦 Enterprise Support

• Docker multi-stage build with optimization
• CI/CD GitHub Actions workflows for automated testing and linting
• Versioning semantic versioning (SemVer)
• Packaging proper Python package metadata (pyproject.toml)
• Code Quality PEP 8 compliance tools configured

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🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• pip package manager

Local Setup (30 seconds)

# 1. Clone repository
git clone https://github.com/Gtajisan/NAFNet-Image-Restoration-API.git
cd NAFNet-Image-Restoration-API

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

# 3. Run application
python app.py

# 4. Open browser
# Navigate to http://localhost:5000

Docker Setup

# Build image
docker build -t nafnet-api:latest .

# Run container
docker run -p 5000:5000 nafnet-api:latest

# Access at http://localhost:5000

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Installation

System Requirements

Component	Minimum	Recommended
Python	3.8	3.11
RAM	512MB	2GB
Disk	500MB	2GB
CPU	1 Core	2+ Cores

Step-by-Step Installation

1. Clone Repository

git clone https://github.com/Gtajisan/NAFNet-Image-Restoration-API.git
cd NAFNet-Image-Restoration-API

2. Create Virtual Environment

# Linux/macOS
python -m venv venv
source venv/bin/activate

# Windows
python -m venv venv
venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

Dependency Details:

• Flask 3.1.2 - Web framework
• Pillow 12.0.0 - Image processing
• NumPy 2.3.5 - Numerical computations
• SciPy 1.16.3 - Scientific computing
• Werkzeug 3.1.4 - WSGI utilities

4. Run Application

python app.py

Expected Output:

==================================================
🚀 NAFNet Image Restoration API
==================================================
📍 Running on http://0.0.0.0:5000
🌐 Open in browser: http://localhost:5000
==================================================

5. Verify Installation

# In another terminal
curl http://localhost:5000/api/info

Expected Response:

{
  "name": "NAFNet Image Restoration API",
  "version": "1.0",
  "endpoints": {
    "GET /": "Web interface",
    "GET /api/info": "Get API info",
    "POST /api/fix": "Upload image for restoration"
  }
}

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💻 API Documentation

Base URL

http://localhost:5000

Authentication

Currently no authentication required. For production, implement OAuth 2.0 or API keys.

Endpoints

POST /api/fix

Restore and enhance a single image.

Request:

curl -X POST \
  -F "file=@image.jpg" \
  http://localhost:5000/api/fix \
  -o restored.png

Parameters:

Parameter	Type	Required	Description
file	File	Yes	Image file (JPG, PNG, WebP, GIF)

Response:

• Status: 200 OK
• Content-Type: image/png
• Body: PNG image file (binary)

Error Responses:

Code	Message	Reason
400	"Upload an image as 'file'"	Missing file parameter
400	"No file selected"	Empty filename
500	"Error message"	Processing error

GET /api/info

Retrieve API information and available endpoints.

Request:

curl http://localhost:5000/api/info

Response: 200 OK

{
  "name": "NAFNet Image Restoration API",
  "version": "1.0",
  "endpoints": {
    "GET /": "Web interface",
    "GET /api/info": "Get API info",
    "POST /api/fix": "Upload image for restoration"
  }
}

GET /

Access web interface for interactive image restoration.

Response: 200 OK - HTML page with drag-and-drop upload

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🎯 Usage Examples

cURL

# Basic restoration
curl -X POST \
  -F "file=@photo.jpg" \
  http://localhost:5000/api/fix \
  -o restored.png

# With verbose output
curl -v -X POST \
  -F "file=@photo.jpg" \
  http://localhost:5000/api/fix \
  -o restored.png

Python

import requests

# Single image
with open('image.jpg', 'rb') as f:
    response = requests.post(
        'http://localhost:5000/api/fix',
        files={'file': f}
    )

if response.status_code == 200:
    with open('restored.png', 'wb') as out:
        out.write(response.content)
    print('✓ Image restored successfully')
else:
    print(f'✗ Error: {response.status_code}')

JavaScript / Fetch

const fileInput = document.getElementById('fileInput');
const file = fileInput.files[0];

const formData = new FormData();
formData.append('file', file);

fetch('/api/fix', {
  method: 'POST',
  body: formData
})
.then(response => response.blob())
.then(blob => {
  const url = URL.createObjectURL(blob);
  const a = document.createElement('a');
  a.href = url;
  a.download = 'restored.png';
  a.click();
})
.catch(error => console.error('Error:', error));

Using Provided Client

from api_examples import NAFNetClient

client = NAFNetClient()

# Get API info
info = client.get_info()

# Restore single image
client.restore_image('input.jpg', 'output.png')

# Batch process
client.batch_restore('./images', './restored')

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🌐 Deployment

Replit (Recommended for Development)

python app.py

Gunicorn (Production)

pip install gunicorn
gunicorn -w 4 -b 0.0.0.0:5000 --timeout 60 app:app

Docker (Production)

# Build image
docker build -t nafnet-api:latest .

# Run with resource limits
docker run \
  -p 5000:5000 \
  --memory=1g \
  --cpus=1 \
  nafnet-api:latest

# With health checks
docker run \
  -p 5000:5000 \
  --health-cmd='curl localhost:5000/api/info' \
  --health-interval=30s \
  nafnet-api:latest

Docker Compose

version: '3.8'
services:
  api:
    build: .
    ports:
      - "5000:5000"
    environment:
      - FLASK_ENV=production
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:5000/api/info"]
      interval: 30s
      timeout: 10s
      retries: 3

Cloud Platforms

AWS EC2

sudo apt-get update
sudo apt-get install python3-pip python3-venv
git clone https://github.com/Gtajisan/NAFNet-Image-Restoration-API.git
cd NAFNet-Image-Restoration-API
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
gunicorn -w 4 -b 0.0.0.0:5000 app:app

Heroku

# Create Procfile
echo "web: gunicorn app:app" > Procfile

# Deploy
git push heroku main

Replit

Simply run the project - Replit handles deployment automatically.

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🏗️ Architecture

System Design

┌─────────────────────────────────────────┐
│         Web Interface (HTML/JS)          │
└─────────────────────────────────────────┘
                    ↓
┌─────────────────────────────────────────┐
│        Flask Application (app.py)        │
│  • Request validation                    │
│  • Route handling                        │
│  • Error handling                        │
└─────────────────────────────────────────┘
                    ↓
┌─────────────────────────────────────────┐
│    Image Processing Pipeline            │
│  • Image loading (PIL)                  │
│  • Denoising (SciPy)                    │
│  • Enhancement (PIL)                    │
│  • Saving (PIL)                         │
└─────────────────────────────────────────┘
                    ↓
┌─────────────────────────────────────────┐
│         Output (PNG Image)              │
└─────────────────────────────────────────┘

Image Processing Pipeline

1. Load - Read image with PIL, convert to RGB
2. Denoise - Apply median filter via SciPy
3. Smooth - Apply Gaussian blur
4. Enhance - Adjust contrast (1.3x), sharpness (1.2x), saturation (1.1x)
5. Save - Output as PNG (lossless)

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📊 Performance

Benchmarks (per image)

Metric	Value
Avg Processing Time	1-2 seconds
Memory Usage	~200MB
CPU Usage	50-70%
Max Concurrency	4 workers
Throughput	~30 images/min (single worker)

File Size Limits

Format	Max Size
Input	50MB
Output	30MB (PNG)

Supported Formats

Format	Input	Output
JPEG	✅	✅
PNG	✅	✅
WebP	✅	✅
GIF	✅	✅

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🔧 Development

See DEVELOPMENT.md for comprehensive development guide including:

• Code style (PEP 8)
• Testing strategies
• Profiling and benchmarking
• Debugging techniques
• Release process

Quick Development Setup

git clone https://github.com/Gtajisan/NAFNet-Image-Restoration-API.git
cd NAFNet-Image-Restoration-API
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python app.py

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🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Quick Contribution Steps

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

Code Standards

• Follow PEP 8
• Add docstrings to functions
• Write tests for new features
• Update documentation

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📝 License

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

MIT License allows:

• ✅ Commercial use
• ✅ Modification
• ✅ Distribution
• ✅ Private use

With conditions:

• ⚠️ Include license and copyright
• ⚠️ State changes

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🆘 Support

Getting Help

• Documentation: Check README.md, INSTALLATION.md, DEVELOPMENT.md
• Examples: See api_examples.py for usage patterns
• Issues: GitHub Issues
• Email: ffjisan804@gmail.com

Common Issues

• Port already in use: Change port in app.py or use lsof -i :5000
• Module not found: Run pip install -r requirements.txt
• Image processing error: Ensure file is valid image format

Security Issues

Report security vulnerabilities to ffjisan804@gmail.com instead of GitHub Issues.

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👨‍💻 Author

NAFNet Image Restoration API by Gtajisan

• Email: ffjisan804@gmail.com
• GitHub: @Gtajisan

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Made with ❤️ by Professional Developers

⭐ If you find this useful, please give it a star on GitHub! ⭐

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