Deep learning for image colorization: Current and future prospects

Abstract

Image colorization, as an essential problem in computer vision (CV), has attracted an increasing amount of researchers attention in recent years, especially deep learning-based image colorization techniques(DLIC). Generally, most recent image colorization methods can be regarded as knowledge-based systems because they are usually trained by big datasets. Unlike the existing reviews, this paper adopts a unique deep learning-based perspective to review the latest progress in image colorization techniques systematically and comprehensively. In this paper, a comprehensive review of recent DLIC approaches from algorithm classification to existing challenges is provided to facilitate researchers’ in-depth understanding of DLIC. In particular, we review DLIC algorithms from various perspectives, including color space, network structure, loss function, level of automation, and application fields. Furthermore, other important issues are discussed, such as publicly available benchmark datasets and performance evaluation metrics. Finally, we discuss several open issues of image colorization and outline future research directions. This survey can serve as a reference for researchers in image colorization and related fields.