论文标题
在双重域中的绘画图像协调
Painterly Image Harmonization in Dual Domains
论文作者
论文摘要
图像协调旨在通过调整前景外观以与背景兼容的前景来产生视觉和谐的复合图像。当复合图像具有摄影前景和绘画背景时，任务称为绘画图像协调。在此任务上只有很少的作品，这些作品既耗时，要么在产生良好的结果时都耗时或弱。在这项工作中，我们提出了一个新型的绘画网络，该网络由双域发生器和双域鉴别器组成，该网络在空间域和频域中都统一了复合图像。双域发生器通过在空间域中使用ADAIN模块以及我们在频域中提出的RESFFT模块进行协调。双域歧视器试图根据每个贴片的空间特征和频率特征来区分inharmonious贴片，这可以增强发电机以对抗性方式增强发电机的能力。基准数据集的广泛实验显示了我们方法的有效性。我们的代码和模型可在https://github.com/bcmi/phdnet-painterly-image-harmonization上找到。
Image harmonization aims to produce visually harmonious composite images by adjusting the foreground appearance to be compatible with the background. When the composite image has photographic foreground and painterly background, the task is called painterly image harmonization. There are only few works on this task, which are either time-consuming or weak in generating well-harmonized results. In this work, we propose a novel painterly harmonization network consisting of a dual-domain generator and a dual-domain discriminator, which harmonizes the composite image in both spatial domain and frequency domain. The dual-domain generator performs harmonization by using AdaIN modules in the spatial domain and our proposed ResFFT modules in the frequency domain. The dual-domain discriminator attempts to distinguish the inharmonious patches based on the spatial feature and frequency feature of each patch, which can enhance the ability of generator in an adversarial manner. Extensive experiments on the benchmark dataset show the effectiveness of our method. Our code and model are available at https://github.com/bcmi/PHDNet-Painterly-Image-Harmonization.
