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Image composition is a fundamental operation in image editing field. However,unharmonious foreground and background downgrade the quality of compositeimage. Image harmonization, which adjusts the foreground to improve theconsistency, is an essential yet challenging task. Previous deep learning basedmethods mainly focus on directly learning the mapping from composite image toreal image, while ignoring the crucial guidance role that background plays. Inthis work, with the assumption that the foreground needs to be translated tothe same domain as background, we formulate image harmonization task asbackground-guided domain translation. Therefore, we propose an imageharmonization network with a novel domain code extractor and well-tailoredtriplet losses, which could capture the background domain information to guidethe foreground harmonization. Extensive experiments on the existing imageharmonization benchmark demonstrate the effectiveness of our proposed method.Code is available at https://github.com/bcmi/BargainNet.

BargainNet: Background-Guided Domain Translation for Image Harmonization