Local color transfer via probabilistic segmentation by expectation-maximization

By Yu-Wing Tai, Jiaya Jia, Chi-Keung Tang. Computer Vision and Pattern Recognition, 2005

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Semantic colorization with internet images (2011, methodology): “This compares favorably to the 48.90% obtained by [Welsh et al. 2002], 32.30% by [Charpiat et al. 2008] and 40.79% by [Tai et al. 2005].”
PixColor: Pixel Recursive Colorization (2017, background): “Early approaches to colorization relied on some amount of human effort, either to identify a relevant source color image from which the colors could be transferred [3, 6, 13, 16, 24, 25, 28, 33, 39], or to get a rough coloring from a human annotator to serve as a set of "hints" [11, 14, 21, 22, 26, ”
Learning Representations for Automatic Colorization (2016, methodology): “Previous colorization methods broadly fall into three categories: scribble-based [15, 21,24,31,44], transfer [3,6,13,19,26,38,41], and automatic direct prediction [5,7].”
Colorization Transformer (2021, background): “…et al., 2017; Ci et al., 2018) and exemplar-based techniques that involve identifying a reference source image to copy colors from (Reinhard et al., 2001; Welsh et al., 2002; Tai et al., 2005; Ironi et al., 2005; Pitié et al., 2007; Morimoto et al., 2009; Gupta et al., 2012; Xiao et al., 2020).”
Visual attribute transfer through deep image analogy (2017, background): “Local color transfer methods infer local color statistics in different color regions by establishing region correspondences [Tai et al. 2005].”