A common problem for composite images is the incompatibility of their foreground and background components. Image harmonization aims to solve this problem, making the whole image look more authentic and coherent. Most existing solutions predict lookup tables (LUTs) or reconstruct images, utilizing various attributes of composite images. Recent approaches have primarily focused on employing global transformations like normalization and color curve rendering to achieve visual consistency, and they often overlook the importance of local visual coherence. We present a patch-based harmonization network consisting of novel Patch-based normalization (PN) blocks and a feature extractor based on statistical color transfer. Extensive experiments demonstrate the network's high generalization capability for different domains. Our network achieves state-of-the-art results on the iHarmony4 dataset. Also, we created a new human portrait harmonization dataset based on FFHQ and checked the proposed method to show the generalization ability by achieving the best metrics on it. The benchmark experiments confirm that the suggested patch-based normalization block and feature extractor effectively improve the network's capability to harmonize portraits. Our code and model baselines are publicly available.

翻译：合成图像的一个常见问题是其前景与背景组件的不兼容性。图像和谐化旨在解决这一问题，使整个图像看起来更加真实和连贯。现有大多数解决方案利用合成图像的各种属性预测查找表（LUTs）或重建图像。近期方法主要侧重于通过归一化、颜色曲线渲染等全局变换实现视觉一致性，却往往忽视了局部视觉连贯性的重要性。我们提出一种基于块的和谐化网络，由新型基于块的归一化（PN）模块和基于统计颜色迁移的特征提取器组成。大量实验表明该网络对不同领域具有高度泛化能力。在iHarmony4数据集上，我们的网络达到了最先进水平。此外，我们基于FFHQ创建了新的肖像和谐化数据集，并通过在该数据集上取得最佳指标验证了所提方法的泛化能力。基准实验证实，所提出的基于块的归一化模块和特征提取器有效提升了网络对肖像的和谐化能力。我们的代码和模型基准已公开提供。