  Image harmonization aims to adjust the foreground illumination in a composite
image to make it harmonious. The existing harmonization methods can only
produce one deterministic result for a composite image, ignoring that a
composite image could have multiple plausible harmonization results due to
multiple plausible reflectances. In this work, we first propose a
reflectance-guided harmonization network, which can achieve better performance
with the guidance of ground-truth foreground reflectance. Then, we also design
a diverse reflectance generation network to predict multiple plausible
foreground reflectances, leading to multiple plausible harmonization results.
The extensive experiments on the benchmark datasets demonstrate the
effectiveness of our method.
