Abstract
In this paper, we propose an efficient algorithm for illuminant estimation. The 2D estimator is derived based on the maximal projections’ mean assumption according to which the estimated illuminant in a chromaticity subspace is the vector maximizing the sum of orthogonal projections of image colors. We show that, the illuminant estimation solution is the eigenvector corresponding to the maximal eigenvalue of the inner-product matrix of data. The 2D estimator is extended to the 3D space by minimizing the reconstruction errors from the 2D estimates obtained in the RGB color space. The evaluation of the 3D resulting estimator on three well-known image datasets generates lower estimation errors.
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