  Most scenes are illuminated by several light sources, where the traditional
assumption of uniform illumination is invalid. This issue is ignored in most
color constancy methods, primarily due to the complex spatial impact of
multiple light sources on the image. Moreover, most existing multi-illuminant
methods fail to preserve the smooth change of illumination, which stems from
spatial dependencies in natural images. Motivated by this, we propose a novel
multi-illuminant color constancy method, by learning pixel-wise illumination
maps caused by multiple light sources. The proposed method enforces smoothness
within neighboring pixels, by regularizing the training with the total
variation loss. Moreover, a bilateral filter is provisioned further to enhance
the natural appearance of the estimated images, while preserving the edges.
Additionally, we propose a label-smoothing technique that enables the model to
generalize well despite the uncertainties in ground truth. Quantitative and
qualitative experiments demonstrate that the proposed method outperforms the
state-of-the-art.
