dixisouls/super_resolution repository overview

⁠Super Resolution: Dockerized

This Docker image provides a complete solution for enhancing image resolution using state-of-the-art EDSR (Enhanced Deep Super Resolution) models with channel attention mechanism. The container packages all necessary components into a lightweight environment, making it seamless to upscale images up to 8x their original resolution.

⁠Features

• Upscales images up to 8x resolution using pre-trained EDSR models
• Incorporates channel attention mechanism for superior feature extraction
• Packages all dependencies and configurations into a single Docker container
• Highly flexible and lightweight, no manual installation required
• Input your low-resolution image, get a high-resolution output instantly

⁠How to Use

⁠1. Pull the Docker Image

Start by pulling the image from Docker Hub:

docker pull dixisouls/super_resolution:latest

⁠2. Run the Container

Run the container to enhance the resolution of your input image:

docker run -v /path/to/input:/input dixisouls/super_resolution:latest /input/image.jpg

⁠Explanation:

• -v /path/to/input:/input: Mounts your local directory (containing the image) into the container. Replace /path/to/input with the absolute path to your folder containing the input image.
• dixisouls/super_resolution:latest: The Docker image pulled from Docker Hub.
• /input/image.jpg: Path to the input image inside the container.
• The enhanced image will be saved as output.png in the same directory as the input image.

⁠Example Usage

If your input image is located in /home/user/images and is named low_res.jpg, you can run:

docker run -v /home/user/images:/input dixisouls/super_resolution:latest /input/low_res.jpg

The high-resolution output will be saved as output.png in the /home/user/images directory.

⁠Repository Structure

This project includes all necessary components for performing image super resolution:

project/
├── Dockerfile                # Docker configuration for environment setup
├── models/                   # Directory containing model architectures
│   ├── edsr.py              # Basic EDSR implementation
│   ├── edsr_deep.py         # Deep EDSR variant
│   └── edsr_channel_attention.py  # EDSR with attention mechanism
├── utils/                    # Utility functions
│   ├── data_utils.py        # Data processing utilities
│   ├── infer_utils.py       # Inference helper functions
│   └── utils.py             # General utilities
├── trained_models/          # Pre-trained model weights directory
│   └── best.pth             # Best model checkpoint
├── config.py                # Training configuration
├── infer_config.py         # Inference configuration
├── inference.py            # Main script for super resolution
└── README.md               # Project documentation

⁠GitHub Repository

You can find the source code for this project on GitHub: Super Resolution GitHub Repository⁠