  We present a deep neural network for removing undesirable shading features
from an unconstrained portrait image, recovering the underlying texture. Our
training scheme incorporates three regularization strategies: masked loss, to
emphasize high-frequency shading features; soft-shadow loss, which improves
sensitivity to subtle changes in lighting; and shading-offset estimation, to
supervise separation of shading and texture. Our method demonstrates improved
delighting quality and generalization when compared with the state-of-the-art.
We further demonstrate how our delighting method can enhance the performance of
light-sensitive computer vision tasks such as face relighting and semantic
parsing, allowing them to handle extreme lighting conditions.
