Illuminant Aware Gamut‐Based Color Transfer

This paper proposes a new approach for color transfer between two images. Our method is unique in its consideration of the scene illumination and the constraint that the mapped image must be within the color gamut of the target image. Specifically, our approach first performs a white‐balance step on both images to remove color casts caused by different illuminations in the source and target image. We then align each image to share the same ‘white axis’ and perform a gradient preserving histogram matching technique along this axis to match the tone distribution between the two images. We show that this illuminant‐aware strategy gives a better result than directly working with the original source and target image's luminance channel as done by many previous methods. Afterwards, our method performs a full gamut‐based mapping technique rather than processing each channel separately. This guarantees that the colors of our transferred image lie within the target gamut. Our experimental results show that this combined illuminant‐aware and gamut‐based strategy produces more compelling results than previous methods. We detail our approach and demonstrate its effectiveness on a number of examples.

[1]  Dani Lischinski,et al.  Optimizing color consistency in photo collections , 2013, ACM Trans. Graph..

[2]  François Pitié,et al.  Automated colour grading using colour distribution transfer , 2007, Comput. Vis. Image Underst..

[3]  Keigo Hirakawa,et al.  Color Constancy with Spatio-Spectral Statistics , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  A.C. Kokaram,et al.  N-dimensional probability density function transfer and its application to color transfer , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[5]  Chi-Keung Tang,et al.  Local color transfer via probabilistic segmentation by expectation-maximization , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[6]  N. N. Krasil'nikov,et al.  Mathematical model of the color constancy of the human visual system , 2002 .

[7]  Brian V. Funt,et al.  A comparison of computational color constancy Algorithms. II. Experiments with image data , 2002, IEEE Trans. Image Process..

[8]  Erik Reinhard,et al.  Progressive color transfer for images of arbitrary dynamic range , 2011, Comput. Graph..

[9]  Miguel Oliveira,et al.  Unsupervised local color correction for coarsely registered images , 2011, CVPR 2011.

[10]  Fabio Pellacini,et al.  User‐Controllable Color Transfer , 2010, Comput. Graph. Forum.

[11]  Youngbae Hwang,et al.  Color Transfer Using Probabilistic Moving Least Squares , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Lizhuang Ma,et al.  Gradient‐Preserving Color Transfer , 2009, Comput. Graph. Forum.

[13]  Joost van de Weijer,et al.  Computational Color Constancy: Survey and Experiments , 2011, IEEE Transactions on Image Processing.

[14]  Joost van de Weijer,et al.  Improving Color Constancy by Photometric Edge Weighting , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Erik Reinhard,et al.  Color Transfer between Images , 2001, IEEE Computer Graphics and Applications.

[16]  Lizhuang Ma,et al.  Color transfer in correlated color space , 2006, VRCIA '06.

[17]  Steven J. Gortler,et al.  The von Kries Hypothesis and a Basis for Color Constancy , 2007, 2007 IEEE 11th International Conference on Computer Vision.