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A review of image and video colorization: From analogies to deep learning

Chen, Shu-Yu, Zhang, Jia-Qi, Zhao, You-You, Rosin, Paul L. ORCID:, Lai, Yu-Kun ORCID: and Gao, Lin 2022. A review of image and video colorization: From analogies to deep learning. Visual Informatics 6 (3) , pp. 51-68. 10.1016/j.visinf.2022.05.003

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Image colorization is a classic and important topic in computer graphics, where the aim is to add color to a monochromatic input image to produce a colorful result. In this survey, we present the history of colorization research in chronological order and summarize popular algorithms in this field. Early works on colorization mostly focused on developing techniques to improve the colorization quality. In the last few years, researchers have considered more possibilities such as combining colorization with NLP (natural language processing) and focused more on industrial applications. To better control the color, various types of color control are designed, such as providing reference images or color-scribbles. We have created a taxonomy of the colorization methods according to the input type, divided into grayscale, sketch-based and hybrid. The pros and cons are discussed for each algorithm, and they are compared according to their main characteristics. Finally, we discuss how deep learning, and in particular Generative Adversarial Networks (GANs), has changed this field.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
ISSN: 2468-502X
Funders: The Royal Society
Date of First Compliant Deposit: 16 June 2022
Date of Acceptance: 13 May 2022
Last Modified: 04 May 2023 02:38

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