Automatic Image Colorization with U-Net and Perceptual Loss

This project features an automatic image colorization model that leverages a U-Net architecture combined with perceptual loss for enhanced colorization quality. The U-Net captures intricate image features through its encoder-decoder structure, while perceptual loss, utilizing a pre-trained VGG network, ensures the model generates visually pleasing and realistic colors by comparing high-level features between the generated and true color images. This approach balances pixel-level accuracy and perceptual realism, leading to superior colorization results. The project includes training scripts and sample results, providing a comprehensive framework for automatic image colorization.

Table of Contents

1.	Introduction
2.	Model Architecture
3.	Perceptual Loss
4.	Installation
5.	Usage

Introduction

Image colorization is a challenging and fascinating problem in computer vision. This project presents a solution using a U-Net architecture combined with perceptual loss, aiming to generate high-quality, realistic color images from grayscale inputs.

Model Architecture

The core of the colorization model is the U-Net architecture, which consists of an encoder-decoder structure. The encoder compresses the input image into a lower-dimensional feature space, capturing essential features, while the decoder reconstructs the image from this compressed representation, adding color in the process.

Perceptual Loss

Perceptual loss is employed to enhance the realism of the colorized images. It uses a pre-trained VGG network to compare high-level features between the generated and true color images. This ensures that the colorization process not only focuses on pixel-level accuracy but also maintains perceptual quality.

Installation

To set up the project, follow these steps:

Clone the repository:

git clone https://github.com/Ayush-Singh677/Image-Colorisation.git
cd Image-Colorisation

Create a virtual environment and activate it:

python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

Install the required dependencies:

pip install -r requirements.txt

Usage

python3 main.py --input grayscale_image.jpg --output colorized_image.jpg --model unet_ploss_vgg19.pth