Fast image and video colorization using chrominance blending

By Liron Yatziv, Guillermo Sapiro. IEEE Transactions on Image Processing, 2006

554 citations recorded by the source corpus.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1e7312678ea3c3a2da6e9a1a56f16b5ef60cf6d2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

Citations in the corpus

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].”
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, ”
Joint bilateral upsampling (2007, background): “These include stereo depth computations [Scharstein and Szeliski 2002], image colorization [Levin et al. 2004; Yatziv and Sapiro 2006], tone mapping of high dynamic range (HDR) images [Reinhard et al. 2005], and applications of minimal graph cuts to image composition [Agarwala et al. 2004].”
Colorization by example (2005, methodology): “Other algorithms based on color scribbles have subsequently been proposed [Sap04, YS04].”
Colorization Transformer (2021, methodology): “…relied on human-in-the-loop 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…”
Nonlocal Linear Image Regularization and Supervised Segmentation (2007, background): “Note that the above algorithm can be related also to image colorization, where a gray-scale image is colored by user-provided coloring examples (see an explanation of the problem and a highly effective solution in [63]).”
A Geodesic Framework for Fast Interactive Image and Video Segmentation and Matting (2007, background): “Our work is inspired by [23], where the authors, following [11], show how to use distance functions for image colorization.”
DeepProp: Extracting Deep Features from a Single Image for Edit Propagation (2016, background): “The ﬁrst studies related to edit propagation are colorizations us-ing optimization [LLW04] and chrominance blending [YS06].”
Geodesic Matting: A Framework for Fast Interactive Image and Video Segmentation and Matting (2009, background): “Moreover, using once again the triangle inequality, the robustness with respect to the actual position of the scribbles can be explicitly computed as well (Yatziv and Sapiro 2006).”
Automatic Image Colorization Via Multimodal Predictions (2008, background): “This last method supposes a definition of the difficulty of the color flow to go through each pixel, usually estimated as a simple function of local greyscale intensity variations, as in Levin et al.[1], in Yatziv and Sapiro [2], or in Horiuchi [3], or by predefined thresholds to detect color edges ”
Tracking Emerges by Colorizing Videos (2018, background): “The task of colorization also been explored in the video domain [35, 36] where methods can explicitly incorporate optical flow to provide temporal coherency or learn to propagate color [37].”
Self-similarity-based image colorization (2014, methodology): “In scribble-based colorization algorithms [1] [2] [3] [4], the user or another automatic method specifies the colors of certain regions of the image in form of either color scribbles or scattered color labels.”
GeoS: Geodesic Image Segmentation (2008, unknown): “11 by repeated segmentation (see also [31]).”
Image colorization using similar images (2012, methodology): “Interactive colorization techniques [9, 7, 18, 15, 13] require a user to manually mark color scribbles on the target image .”