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.
@article{arxiv.1911.13239,
title = {DoveNet: Deep Image Harmonization via Domain Verification},
author = {Wenyan Cong and Jianfu Zhang and Li Niu and Liu Liu and Zhixin Ling and Weiyuan Li and Liqing Zhang},
journal= {arXiv preprint arXiv:1911.13239},
year = {2020}
}
Comments
Accepted by CVPR2020. arXiv admin note: text overlap with arXiv:1908.10526