Automated colour grading using colour distribution transfer

By François Pitié, Anil C. Kokaram, Rozenn Dahyot. Computer Vision and Image Understanding, 2007

460 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/787874fedd7384be5b04530bf9334cb58e0c1bd1

How researchers see this paper

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

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Let there be color! (2016, methodology): “For recolor-ing a color image, color transfer techniques [Reinhard et al. 2001; Tai et al. 2005; Piti´e et al. 2007; Wu et al. 2013] are widely used.”
Wasserstein Barycenter and Its Application to Texture Mixing (2011, methodology): “Indeed, it allows various image modiﬁcations, such as e.g. color transfer [3], texture mapping [4], or contrast equalization of video [5] (see [6] for other applications).”
Orthogonal Estimation of Wasserstein Distances (2019, methodology): “…is a strong connection between orthogonal coupling of projection directions and notions of stratiﬁed sampling; (ii) contrary to the intuition of Piti´e et al. (2007), using orthogonal projection directions can actually worsen the performance of sliced Wasserstein estimation (as measured by…”
Unidimensional and Evolution Methods for Optimal Transportation (2013, methodology): “is so easy to compute in dimension one, and so di cult to get in higher dimensions, the image processing community has devised a way to build another transport map, using only unidimensional mappings [49, 50]—not unlike Knothe’s rearrangement therefore, but without its greatest aw, which is its bein”
Synthesizing structured image hybrids (2010, methodology): “by using existing color transfer techniques [Pitié et al. 2007].”
Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn's Algorithm (2018, background): “On the other hand, in image color transfer (Pitié et al., 2007) or domain adaptation (Courty et al.”
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).”
Enhancing Photorealism Enhancement (2021, methodology): “Subsequent work introduced increasingly sophisticated techniques for photographic style transfer between images [18], [48], [49], [50], [51], [52], [53], [54].”
Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables (2017, background): “The underlying N-pdft algorithm also has proven convergence properties (Pitié et al. 2007).”
Wasserstein regularization of imaging problem (2011, unknown): “Most approaches (see for instance [2] [3] [4] [5] for gray level or color modification) are based on histogram manipulation (e.g. histogram equalization, affine transformation, etc).”
{Euclidean, metric, and Wasserstein} gradient flows: an overview (2016, background): “uniqueness is proven under very mild assumptions on the drift (or, equivalently, on the potential), provided some diﬀusion is added. Sliced Wasserstein distance Inspired by a construction proposed in [77] for image impainting, M. Bernot proposed around 2008 an interesting scheme to build a transport”
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, ”
Scaling Algorithms for Unbalanced Transport Problems (2016, background): “Optimal transport between histograms has proven useful for problems of this sort such as contrast adjustment [28] and color transfer via 1D transportation [60].”
Optimal Transport for Applied Mathematicians: Calculus of Variations, PDEs, and Modeling (2015, methodology): “Many features are in common with the approach of [252].”
Computational Optimal Transport (2018, methodology): “, 2011] and is related to the alternate minimization approach detailed in [Pitié et al., 2007].”