As an important subtopic of image enhancement, color transfer aims to enhance the color scheme of a source image according to a reference one while preserving the semantic context. To implement color transfer, the palette-based color mapping framework was proposed. \textcolor{black}{It is a classical solution that does not depend on complex semantic analysis to generate a new color scheme. However, the framework usually requires manual settings, blackucing its practicality.} The quality of traditional palette generation depends on the degree of color separation. In this paper, we propose a new palette-based color transfer method that can automatically generate a new color scheme. With a redesigned palette-based clustering method, pixels can be classified into different segments according to color distribution with better applicability. {By combining deep learning-based image segmentation and a new color mapping strategy, color transfer can be implemented on foreground and background parts independently while maintaining semantic consistency.} The experimental results indicate that our method exhibits significant advantages over peer methods in terms of natural realism, color consistency, generality, and robustness.

翻译：作为图像增强的一个重要子课题，颜色迁移旨在根据参考图像增强源图像的色彩方案，同时保持语义上下文。为实现颜色迁移，研究人员提出了基于调色板的颜色映射框架。\textcolor{black}{该框架是一种经典解决方案，无需依赖复杂的语义分析即可生成新的色彩方案。然而，该框架通常需要手动设置，降低了其实用性。}传统调色板生成的质量取决于颜色分离的程度。本文提出一种新的基于调色板的颜色迁移方法，可自动生成新的色彩方案。通过重新设计的基于调色板的聚类方法，像素可根据颜色分布被划分为不同区域，具有更好的适用性。{通过结合基于深度学习的图像分割与新的颜色映射策略，可在保持语义一致性的前提下，对前景和背景部分独立实现颜色迁移。}实验结果表明，本方法在自然逼真度、颜色一致性、泛化能力和鲁棒性方面均显著优于同类方法。