  Image composition plays a common but important role in photo editing. To
acquire photo-realistic composite images, one must adjust the appearance and
visual style of the foreground to be compatible with the background. Existing
deep learning methods for harmonizing composite images directly learn an image
mapping network from the composite to the real one, without explicit
exploration on visual style consistency between the background and the
foreground images. To ensure the visual style consistency between the
foreground and the background, in this paper, we treat image harmonization as a
style transfer problem. In particular, we propose a simple yet effective
Region-aware Adaptive Instance Normalization (RAIN) module, which explicitly
formulates the visual style from the background and adaptively applies them to
the foreground. With our settings, our RAIN module can be used as a drop-in
module for existing image harmonization networks and is able to bring
significant improvements. Extensive experiments on the existing image
harmonization benchmark datasets show the superior capability of the proposed
method. Code is available at {https://github.com/junleen/RainNet}.
