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Papers/DoveNet: Deep Image Harmonization via Domain Verification

DoveNet: Deep Image Harmonization via Domain Verification

Wenyan Cong, Jianfu Zhang, Li Niu, Liu Liu, Zhixin Ling, Weiyuan Li, Liqing Zhang

2019-11-27CVPR 2020 6Image Harmonization
PaperPDFCode(official)

Abstract

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.

Results

TaskDatasetMetricValueModel
Image GenerationiHarmony4MSE52.33DoveNet
Image GenerationiHarmony4PSNR34.76DoveNet
Image GenerationiHarmony4fMSE549.96DoveNet
Image GenerationHAdobe5k(1024$\times$1024)MSE51DoveNet
Image GenerationHAdobe5k(1024$\times$1024)PSNR34.81DoveNet
Image GenerationHAdobe5k(1024$\times$1024)SSIM0.9729DoveNet
Image GenerationHAdobe5k(1024$\times$1024)fMSE312.88DoveNet

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