Palette-based Color Transfer between Images

TL;DR

This paper introduces an automatic palette-based color transfer method that uses deep learning segmentation and improved clustering to enhance image color schemes while preserving semantic content, outperforming existing methods.

Contribution

It presents a novel automatic palette generation and color transfer approach combining deep learning segmentation with a new clustering strategy.

Findings

Outperforms peer methods in realism and color consistency

Automatically generates palettes without manual intervention

Maintains semantic integrity during color transfer

Abstract

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.