Image Restoration Project

Introduction

This project, developed as a part of our final year project, aims to provide a web application for image restoration, focusing primarily on image colorization. Utilizing advanced techniques and algorithms, we strive to digitally recreate images, enhancing their quality and color. The application is built using Streamlit, making it easily accessible and user-friendly.

Features

• Image Colorization: Convert black and white images to color.
• Interactive UI: Easy-to-use interface with drag and drop functionality.
• Image Conversion: Convert images to LAB format and separate Lab components.
• Installation
• Before running the application, ensure you have Python installed on your system. Then, follow these steps to install the required libraries.

Installation

Before running the application, ensure you have Python installed on your system. Then, follow these steps to install the required libraries.

git clone github.com/shubhamshnd/Image-Colorisation

pip install -r requirements.txt

Usage

To run the application, execute the following command in the terminal:

streamlit run frontend.py

Once the application is running, navigate through the sidebar to access different features:

-Welcome: Introduction and overview of the application. -Image Colorization: Upload an image for colorization. -Team: Information about the team behind the project.

How It Works

Uploading an Image: Users can upload an image in PNG, JPG, or JPEG format. The uploaded image is saved locally for processing. Image Colorization: The application converts the uploaded image to LAB format, allows users to adjust components using a slider, and then colorizes the image using a pre-trained model. Displaying Results: The application displays the original, LAB converted, component-adjusted, and final colorized images.

Advanced Usage: Programmatic Image Colorization

For those interested in the technical details or wishing to integrate the colorization process into their own Python scripts, here is a basic example. This code demonstrates how to use OpenCV and a pre-trained model to colorize black and white images.

pip install opencv-python numpy

python b2w.py

Remember to add your own image to the path in the b2w.py code

# Load your image
image = cv2.imread('./path/to/image')

Disclaimer

This script is a simple demonstration. For production use or more complex applications, additional error handling and optimizations may be required.