  Given a composite image, image harmonization aims to adjust the foreground
illumination to be consistent with background. Previous methods have explored
transforming foreground features to achieve competitive performance. In this
work, we show that using global information to guide foreground feature
transformation could achieve significant improvement. Besides, we propose to
transfer the foreground-background relation from real images to composite
images, which can provide intermediate supervision for the transformed encoder
features. Additionally, considering the drawbacks of existing harmonization
datasets, we also contribute a ccHarmony dataset which simulates the natural
illumination variation. Extensive experiments on iHarmony4 and our contributed
dataset demonstrate the superiority of our method. Our ccHarmony dataset is
released at https://github.com/bcmi/Image-Harmonization-Dataset-ccHarmony.

Computer Vision and Pattern Recognition