A Study of Advancements in Deep Learning-Based Image Colorization Techniques

This paper provides an overview of recent advancements in deep learning(DL)-based image colorization techniques. It highlights the importance of image colorization in various domains and the limitations of traditional manual methods. The emergence of deep learning approaches, along with the use of large-scale datasets, has greatly improved colorization performance. Preprocessing techniques, loss functions, and evaluation metrics are discussed. Notable architectures like CEDN, U-Net, and Deep Koalarization are presented, showcasing their unique features. The challenges in image colorization and future research directions are identified. The potential impact of image colorization in fashion, interior design, and advertising is highlighted. Ongoing research in this field is essential for further advancements and its transformative potential in visual content creation.