Image colorization using similar images

By Raj Kumar Gupta, Alex Yong Sang Chia, Deepu Rajan, Ee Sin Ng, Zhiyong Huang. 2012

290 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Generative Models

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Citations in the corpus

Colorful Image Colorization (2016, methodology): “First, color reference images are provided by the user [3,6,7] or automatically retrieved with user-supplied metadata [8,9].”
Learning Representations for Automatic Colorization (2016, methodology): “However, most methods still require some level of user input [3, 6, 13, 19, 21, 33].”
Real-time user-guided image colorization with learned deep priors (2017, methodology): “Early semi-automatic methods (Chia et al. 2011; Gupta et al. 2012; Irony et al. 2005; Liu et al. 2008; Welsh et al. 2002) utilize an example-based approach that transfers color statistics from a reference image or multiple images (Liu et al. 2014; Mori-moto et al. 2009) to the input grayscale image…”
Deep Colorization (2015, methodology): “6: Comparison with the state-of-art colorization methods [7], [9], [10], [12].”
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, ”
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).”