Palette-based Color Transfer between Images
<|reference_start|>Palette-based Color Transfer between Images: 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.<|reference_end|>
@article{lv2024palette-based,
    title={Palette-based Color Transfer between Images},
    author={Chenlei Lv, Dan Zhang},
    journal={arXiv preprint arXiv:2405.08263},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.08263},
    primaryClass={cs.CV}
}