Color transfer aims to change the color information of the target image according to the reference one. Many studies propose color transfer methods by analysis of color distribution and semantic relevance, which do not take the perceptual characteristics for visual quality into consideration. In this study, we propose a novel color transfer method based on the saliency information with brightness optimization. First, a saliency detection module is designed to separate the foreground regions from the background regions for images. Then a dual-branch module is introduced to implement color transfer for images. Finally, a brightness optimization operation is designed during the fusion of foreground and background regions for color transfer. Experimental results show that the proposed method can implement the color transfer for images while keeping the color consistency well. Compared with other existing studies, the proposed method can obtain significant performance improvement. The source code and pre-trained models are available at https://github.com/PlanktonQAQ/SCTNet .

中文翻译：

---

显着性引导深度神经网络通过光优化实现颜色传输

颜色迁移的目的是根据参考图像改变目标图像的颜色信息。许多研究通过分析颜色分布和语义相关性提出颜色迁移方法，但没有考虑视觉质量的感知特征。在本研究中，我们提出了一种基于显着性信息和亮度优化的新颖颜色转移方法。首先，设计显着性检测模块来将图像的前景区域与背景区域分开。然后引入双分支模块来实现图像的颜色传输。最后，在融合前景和背景区域以进行颜色传递期间设计了亮度优化操作。实验结果表明，该方法能够实现图像的颜色迁移，同时保持良好的颜色一致性。与其他现有研究相比，所提出的方法可以获得显着的性能提升。源代码和预训练模型可在以下位置获取https://github.com/PlanktonQAQ/SCTNet 。