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Super-Resolution GAN (SRGAN) Implementation

About The Project

This project is a Python-based implementation of the Super-Resolution Generative Adversarial Network (SRGAN) using PyTorch. SRGAN is a pivotal development in the field of computer vision, enabling the enhancement of image resolution beyond traditional methods. By leveraging adversarial networks, SRGAN attempts to reconstruct low-resolution images into high-definition outputs. This repository not only implements the core technology but also provides tools for training and testing the model with custom datasets.

Built With

This section lists the major frameworks and libraries that you need to run the project:

  • Python - The programming language used.
  • PyTorch - The deep learning framework.
  • OpenCV - For image manipulation operations.
  • NumPy - For high-performance scientific computing.

Getting Started

Prerequisites

Ensure you have the following installed on your system:

  • Python 3.8 or newer
  • pip (Python package installer)
sudo apt-get install python3-pip

Installation

To set up the project locally, follow these steps:

  1. Clone the repository

    git clone https://github.com/iamFury2K/SR-GAN_Scratch.git
cd SR-GAN_Scratch

  2. Install required Python libraries

    pip install -r requirements.txt

Usage

Here's how you can use this project to upscale images:

  1. Prepare your dataset

    Place your low-resolution images in a folder named data/input.

  2. Train the model

    To train the model on your dataset, run:

    python train.py

    Adjust the parameters as necessary.

Contributing

We welcome contributions from the community. Here are some ways you can contribute:

  • Reporting bugs
  • Suggesting enhancements
  • Writing code for fixed bugs or added features
  • Improving documentation

To contribute, please follow these guidelines:

  1. Fork the project repository.
  2. Create a new branch for your feature or fix (git checkout -b feature/YourFeature or git checkout -b fix/YourBugFix).
  3. Commit your changes with a descriptive message.
  4. Push the branch to your fork.
  5. Open a pull request to our project.

License

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

Acknowledgments

  • Aladdin Persson for foundational resources and tutorials.
  • Your Tutorial Link - For providing the tutorial that inspired this project.
  • Thanks to all contributors who have participated in this project.

This README is structured to provide all necessary information to get started, understand, and contribute to the project effectively.