NAFNet-Image-Restoration-API

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](#quick-start) • [Installation](#installation) • [API Docs](#-api-documentation) • [Examples](#-usage-examples) • [Deployment](#-deployment) • [Contributing](#-contributing)

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

✨ 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

Denoise - Median filtering to remove noise while preserving edges
Smooth - Gaussian blur for artifact reduction
Enhance Contrast - 1.3x multiplier for visual impact
Sharpen - 1.2x multiplier for edge definition
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

🚀 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

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"
  }
}

💻 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

Response:

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

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

🎯 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')

🌐 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.

🏗️ 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

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

📊 Performance

Benchmarks (per image)

File Size Limits

Supported Formats

| Format | Input | Output | |——–|——-|——–| | JPEG | ✅ | ✅ | | PNG | ✅ | ✅ | | WebP | ✅ | ✅ | | GIF | ✅ | ✅ |

🔧 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

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Quick Contribution Steps

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

Code Standards

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

📝 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

🆘 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.

👨‍💻 Author

NAFNet Image Restoration API by Gtajisan

Email: ffjisan804@gmail.com
GitHub: @Gtajisan

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