@article{Pitie_CVIU2007, 
title =  {Automated colour grading using colour distribution transfer}, 
journal =  {Computer Vision and Image Understanding},
volume =  {107}, 
number =  {1}, 
pages =  {123 - 137},
year =  {2007}, 
note =  {Special issue on color image processing},
issn =  {1077-3142}, 
doi =  {10.1016/j.cviu.2006.11.011}, 
note = {Github: https://github.com/frcs/colour-transfer },
abstract = {This article proposes an original method for grading the colours between different images or shots.
The first stage of the method is to find a one-to-one colour mapping that transfers the palette of an example target picture to the original picture.
This is performed using an original and parameter free algorithm that is able to transform any N-dimensional probability density function into another one.
The proposed algorithm is iterative, non-linear and has a low computational cost. Applying the colour mapping on the original picture allows reproducing
the same ‘feel’ as the target picture, but can also increase the graininess of the original picture, especially if the colour dynamic of the two pictures
is very different. The second stage of the method is to reduce
this grain artefact through an efficient post-processing algorithm that intends to preserve the gradient field of the original picture.},
url = {https://mural.maynoothuniversity.ie/15125/1/RD_automated.pdf},
eprint =  {http://www.sciencedirect.com/science/article/pii/S1077314206002189},
author =  {François Pitie and Anil C. Kokaram and Rozenn Dahyot}, 
keywords =  {Colour grading, Colour transfer, Re-colouring, Distribution transfer}}

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