Palette-based Color Transfer between Images
This work addresses the need for automated and practical color transfer in image enhancement, offering an incremental improvement over manual palette-based methods.
The paper tackled the problem of automating color transfer between images by proposing a new palette-based method that independently processes foreground and background using deep learning segmentation and a new color mapping strategy, resulting in significant advantages in natural realism, color consistency, generality, and robustness over peer methods.
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.