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.

自动生成新的调色方案的调色迁移方法，通过重新设计的基于调色板的聚类方法和基于深度学习的图像分割的结合，实现了前景和背景部分的独立调色迁移，同时保持语义一致性。实验结果表明，该方法在自然逼真度、颜色一致性、普适性和鲁棒性等方面优于同类方法。