Deep Image Harmonization via Domain Verification

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 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 with a novel domain verification discriminator, enlightened by the following insight. Specifically, incompatible foreground and background belong to two different domains, so we need to translate the domain of foreground to the same domain as background. Our proposed domain verification discriminator can play such a role by pulling close the domains of foreground and background. Extensive experiments on our constructed dataset demonstrate the effectiveness of our proposed method. Our dataset is released in https://github.com/bcmi/Image_Harmonization_Datasets.