Image Colorization

Abstract

Abstract: Image colorization is a complex and challenging task in computer vision, with numerous applications in art, entertainment, restoration, and more. This project aims to develop an automated image colorization system leveraging the power of deep learning techniques. The primary objective is to train a deep neural network model capable of accurately and semantically colorizing grayscale images, reproducing natural and visually appealing colour distributions. Our approach utilizes Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) to learn the intricate relationships between grayscale images and their corresponding colour versions. The project involves the following key steps: Data Collection and Preprocessing, Model architecture, Training, Evaluation, Application. The project's outcome is expected to provide a powerful and versatile tool for automating image colorization tasks, offering high-quality results while preserving the artistic intent of the original images. The fusion of deep learning and computer vision techniques in this project exemplifies the potential for artificial intelligence to revolutionize image processing and creative industries.