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AI Image Enhancement with Jupyter Notebook and Anaconda

Python

Description

The AI Image Enhancement repository provides Jupyter Notebook examples and Anaconda environment files for utilizing artificial intelligence techniques to enhance digital images. This project aims to demonstrate various image enhancement algorithms and their implementation using Python and Jupyter Notebooks.

Table of Contents

Introduction

Image enhancement plays a crucial role in improving the visual quality of images, reducing noise, and increasing the level of details. This repository serves as a learning resource and experimentation ground for AI-powered image enhancement techniques. It covers various methods, such as:

  • Contrast Enhancement: Techniques to adjust the contrast of an image, making it more visually appealing.
  • Denoising Algorithms: AI-based denoising techniques to reduce noise from images.
  • Super-Resolution: Enhancing image resolution using deep learning models.
  • Colorization: AI-powered colorization of grayscale images.

Installation

To run the Jupyter Notebooks in this repository, follow these steps:

  1. Install Anaconda on your system if you haven't already.

  2. Clone this repository to your local machine:

    git clone https://github.com/<your_username>/AI-Image-Enhancement.git

  3. Navigate to the repository directory:

    cd AI-Image-Enhancement

  4. Create a new Anaconda environment with the required dependencies:

    conda env create -f environment.yml

  5. Activate the newly created environment:

    conda activate ai-enhance

Usage

After setting up the environment, launch Jupyter Notebook:

jupyter notebook

Open the Jupyter Notebook web interface in your browser, navigate to the notebooks/ directory, and explore the various notebooks available. Each notebook covers a specific image enhancement technique, provides code examples, and explanations of the underlying AI algorithms.

Contributing

Contributions to this repository are welcome! If you have new image enhancement techniques or improvements to the existing code, please open a pull request. For major changes or feature additions, it's best to discuss the changes in an issue before making modifications.

License

This project is licensed under the MIT License. You are free to use, modify, and distribute the code as long as you retain the original license information.

Acknowledgments

Special thanks to the open-source community and contributors who have inspired and supported the development of this project.