  Image composition is an important operation in image processing, but the
inconsistency between foreground and background significantly degrades the
quality of composite image. Image harmonization, aiming to make the foreground
compatible with the background, is a promising yet challenging task. However,
the lack of high-quality publicly available dataset for image harmonization
greatly hinders the development of image harmonization techniques. In this
work, we contribute an image harmonization dataset iHarmony4 by generating
synthesized composite images based on COCO (resp., Adobe5k, Flickr, day2night)
dataset, leading to our HCOCO (resp., HAdobe5k, HFlickr, Hday2night)
sub-dataset. Moreover, we propose a new deep image harmonization method DoveNet
using a novel domain verification discriminator, with the insight that the
foreground needs to be translated to the same domain as background. Extensive
experiments on our constructed dataset demonstrate the effectiveness of our
proposed method. Our dataset and code are available at
https://github.com/bcmi/Image_Harmonization_Datasets.
