Colorization of Black and White Images Using Machine Learning

Abstract

This study explores the application of deep learning techniques for the colorization of black-and-white images. Leveraging the capabilities of Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), the research aims to develop an innovative model that can accurately and realistically add color to grayscale images. The project seeks to address the challenges of colorization, such as preserving texture details and maintaining natural color transitions. By harnessing the power of deep learning, the study endeavours to provide a transformative solution for enhancing visual content and historical imagery. The proposed model holds the potential to revolutionize the field of image processing and restoration, offering new possibilities for creative expression and historical preservation. Additionally, the research investigates novel approaches to overcome common issues encountered in traditional colorization methods, including alleviating color bleeding and enhancing edge accuracy. Furthermore, the study explores the integration of semantic information to improve the contextual understanding of the images, thus achieving more coherent and accurate colorization results. Through these advancements, this research contributes to advancing state-of-the-art image colorization techniques, paving the way for broader applications in various domains such as film restoration, digital art, and archival preservation.