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Color constancy seeks to keep the perceived color of objects consistent under varying illumination conditions. However, existing methods often rely on restrictive prior assumptions or suffer from limited generalization capability, posing significant challenges in complex scenes with multiple light sources. In this paper, we propose a neural network-enhanced, physicsbased approach to multi-illuminant color constancy that leverages spectral imaging—highly sensitive to illumination variation. First, we analyze the physical image-formation process under mixed lighting and introduce a master–subordinate illumination model, extending conventional correlated-color-temperature reillumination techniques. Our neural network framework ex-plicitly models the correlation between narrow-band spectral reflectance and the spectral power distribution (SPD) of the illumination, enabling accurate recovery of the scene light's full SPD. Using this model, we fuse RGB images with the estimated illumination spectra to predict illuminant chromaticity precisely, then correct image colors to a standard reference light. Extensive experiments on synthetic multi–color-temperature datasets and real-world spectral datasets demonstrate that our neural network-based method achieves state-of-the-art accuracy in spectral estimation and color-constancy correction.
Xue et al. (Wed,) studied this question.