The code is provided for the following publications:
[1] Gong, H., Finlayson, G.D., Fisher, R.B. and Fang, F., 2017. 3D color homography model for photo-realistic color transfer re-coding. The Visual Computer, pp.1-11.
[2] Gong, H., Finlayson, G.D., Fisher, R.B.: Recoding color transfer as a color homography. In: British Machine Vision Conference. BMVA (2016)
Please cite the above papers if you find them useful for your research.
Copyright 2018 Han Gong (gong@fedoraproject.org), University of {East Anglia, Edinburgh}.
Color transfer is an image editing process that naturally transfers the color theme of a source image to a target image. In this paper, we propose a 3D color homography model which approximates photo-realistic color transfer algorithm as a combination of a 3D perspective transform and a mean intensity mapping. A key advantage of our approach is that the re-coded color transfer algorithm is simple and accurate. Our evaluation demonstrates that our 3D color homography model delivers leading color transfer re-coding performance. In addition, we also show that our 3D color homography model can be applied to color transfer artifact fixing, complex color transfer acceleration, and color-robust image stitching.
cf_3D_H.m: the code of the TVC paper [1].cf_2D_H.m: the code of the BMVC paper [2].
test_main.m: this file contains the evaluation code for re-producing the results in the paper.
cf_MK.m and cf_Poly.m are the implementations for two other color transfer approximation methods for comparison. The approximation outputs will be saved in the folder out_pair.
A set of 200 transfer cases for 4 different color transfer methods (800 test cases in total) are contained in the folder in_pair. Note that the code for the original color transfer methods is not provided here. Only the cached results for color transfer approximation are provided in the dataset.
Rules of Filenames:
*_s.jpg: original source image.*_s.jpg: original target image.*_[method_name].jpg: color transfer result produced by [method_name].
The proposed method may be used for (see the original paper for higher-quality images):
- color transfer artifact fixing
- complex color transfer acceleration
- color-robust image stitching.
