论文标题
讨价还价：图像协调的背景引导域翻译
BargainNet: Background-Guided Domain Translation for Image Harmonization
论文作者
论文摘要
图像组成是图像编辑字段中的基本操作。但是，不受伤害的前景和背景降低了复合图像的质量。图像协调可以调整前景以提高一致性，这是一项必不可少但具有挑战性的任务。以前的基于深度学习的方法主要集中于直接学习从复合图像到真实图像的映射，同时忽略了背景扮演的关键指导角色。在这项工作中，假设前景需要将其转换为与背景相同的域，我们将图像协调任务作为背景引导的域翻译。因此，我们提出了一个具有新颖的域代码提取器和精心定制的三重态损失的图像协调网络，该网络可以捕获背景域信息以指导前景和谐。对现有图像协调基准的广泛实验证明了我们提出的方法的有效性。代码可在https://github.com/bcmi/bargainnet上找到。
Image composition is a fundamental operation in image editing field. However, unharmonious foreground and background downgrade the quality of composite image. Image harmonization, which adjusts the foreground to improve the consistency, is an essential yet challenging task. Previous deep learning based methods mainly focus on directly learning the mapping from composite image to real image, while ignoring the crucial guidance role that background plays. In this work, with the assumption that the foreground needs to be translated to the same domain as background, we formulate image harmonization task as background-guided domain translation. Therefore, we propose an image harmonization network with a novel domain code extractor and well-tailored triplet losses, which could capture the background domain information to guide the foreground harmonization. Extensive experiments on the existing image harmonization benchmark demonstrate the effectiveness of our proposed method. Code is available at https://github.com/bcmi/BargainNet.
