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, which aims to make the foreground compatible with the background, is a promising yet challenging task. However, the lack of high-quality public dataset for image harmonization, which significantly hinders the development of image harmonization techniques. Therefore, we contribute an image harmonization dataset iHarmony4 by generating synthesized composite images based on existing COCO (resp., Adobe5k, day2night) dataset, leading to our HCOCO (resp., HAdobe5k, Hday2night) sub-dataset. To enrich the diversity of our dataset, we also generate synthesized composite images based on our collected Flick images, leading to our HFlickr sub-dataset. The image harmonization dataset iHarmony4 is released at https://github.com/bcmi/Image_Harmonization_Datasets.
@article{arxiv.1908.10526,
title = {Image Harmonization Dataset iHarmony4: HCOCO, HAdobe5k, HFlickr, and Hday2night},
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:1908.10526},
year = {2020}
}
Comments
Our full paper arXiv:1911.13239 "DoveNet: Deep Image Harmonization via Domain Verification" is accepted by CVPR2020