Generalized Gamut Mapping using Image Derivative Structures for Color Constancy
International Journal of Computer Vision
Color Constancy Using Natural Image Statistics and Scene Semantics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Color Constancy: Survey and Experiments
IEEE Transactions on Image Processing
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Identification of illumination is an important problem in imaging. In this paper we present a new and effective physics-based colour constancy algorithm which makes use of a novel log-relative-chromaticity planar constraint. We call the new feature the Zeta-image. We show that this new feature is tied to a novel application of the Kullback-Leibler Divergence, here applied to chromaticity values instead of probabilities. The new method requires no training data or tunable parameters. Moreover it is simple to implement and very fast. Our experimental results across datasets of real images show the proposed method significantly outperforms other unsupervised methods while its estimation accuracy is comparable with more complex, supervised, methods. As well, the new planar constraint can be used as a post-processing stage for any candidate colour constancy method in order to improve its accuracy.