"深度学习技术在图像作色中的应用：调查与数据集"

Image colorization is a crucial aspect of image processing and computer vision, allowing for the enhancement and beautification of images and videos by adding color to grayscale images. In recent years, deep learning techniques have greatly advanced the field of image colorization. This article provides a detailed overview of the latest state-of-the-art colorization methods that employ deep learning algorithms. These algorithms have revolutionized the way images are colorized by significantly improving both accuracy and efficiency. The article delves into the fundamental block architectures utilized in these algorithms, shedding light on the underlying mechanisms that drive their success. By examining the various deep learning algorithms used in image colorization, this survey aims to provide a comprehensive understanding of the techniques and methodologies that have been developed in recent years. The article also discusses the challenges faced in colorization tasks and the solutions proposed by researchers to overcome these obstacles. One of the key contributions of this survey is the introduction of a new dataset specifically designed for image colorization tasks. This dataset aims to address the limitations of existing datasets by providing a more diverse and comprehensive set of images for training and evaluation purposes. By incorporating this dataset into their research, scholars and practitioners in the field will have access to a richer and more expansive set of data for their experiments and developments. In conclusion, image colorization continues to be a vibrant and rapidly evolving field within image processing and computer vision. The advancements made in deep learning techniques have significantly improved the colorization process, making it more accurate and efficient. This survey and dataset provide valuable insights into the current state of image colorization and pave the way for future research and innovation in this exciting field.