Manga colorization

By Yingge Qu, Tien-Tsin Wong, P. Heng. ACM Transactions on Graphics, 2006

349 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e9d41f3e7af234b05102148e1ab72d30713cc0e5

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In Atomic and Molecular Physics, and Optics

In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

Citations in the corpus

AppProp: all-pairs appearance-space edit propagation (2008, methodology): “Methods based on color labeling have been proposed to address each of these limitations, respectively [Irony et al. 2005] and [Qu et al. 2006; Luan et al. 2007], but remain limited due to the often present errors in the labeling process that cause artifacts during edit propagation.”
Deep exemplar-based colorization (2018, background): “Further advances extended similarity to textures [Qu et al. 2006; Luan et al. 2007], intrinsic distance [Yatziv and Sapiro 2006], and exploited edges to reduce color bleeding [Huang et al.”
Image colorization using similar images (2012, background): “To reduce the number of color scribbles, Qu [15] and Luan [13] propagated scribbles that are marked on an image patch to other patches that have similar features.”
Colorization Transformer (2021, background): “…approaches to provide hints in the form of scribbles (Levin et al., 2004; Ironi et al., 2005; Huang et al., 2005; Yatziv & Sapiro, 2006; Qu et al., 2006; Luan et al., 2007; Tsaftaris et al., 2014; Zhang et al., 2017; Ci et al., 2018) and exemplar-based techniques that involve…”
Let there be color! (2016, methodology): “Texture classification has also been used for cartoon colorization [Qu et al. 2006].”
Sketch-based manga retrieval using manga109 dataset (2015, background): “Several applications for manga images have been proposed: colorization [40], [41], [42], vectorization [43], layout recognition [44], layout generation [45], [46], element composition [47], manga-like rendering [48], speech balloon detection [49], segmentation [50], face tailored features [51],…”
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): “historical photographs and videos [40], artist assistance [31, 37]), much research strives to make it cheaper and less time-consuming [3,5–7,13,19,21,26,41].”
Scribbler: Controlling Deep Image Synthesis with Sketch and Color (2016, background): “Similar control strokes have been applied to sketch and manga imagery [36, 45], but the results remain nonphotorealistic and lack lighting and shading.”
Deep Colorization (2015, methodology): “Colorization methods can be roughly divided into two categories: scribble-based colorization [2], [3], [4], [5], [6] and example-based colorization [7], [8], [9], [10], [11], [12].”