用语义调色板实现精准局部颜色编辑
Semantic Palette-Guided Color Propagation
- 基于语义提取调色板，按内容相似性传播颜色变化
- 通过能量函数优化，实现局部编辑的准确传播
- 适合需要保留细节的图像精细调色任务
颜色传播旨在将局部颜色修改扩展到图像中相似区域。传统方法依赖颜色、纹理或明度等低层视觉特征衡量像素相似性，难以实现内容感知的颜色传播。尽管部分近期方法引入了语义信息，但常导致不自然的全局颜色改变。为此，本文提出一种语义调色板引导的颜色传播方法：首先从输入图像中提取语义调色板；然后基于用户编辑，通过最小化设计的能量函数求解修改后的调色板；最后利用求解出的调色板将局部编辑精准传播至具有相似语义的区域。该方法实现了高效且精确的像素级颜色编辑，并确保颜色变化以内容感知的方式传播。大量实验验证了方法的有效性。
原文摘要 · Abstract (English)
Color propagation aims to extend local color edits to similar regions across the input image. Conventional approaches often rely on low-level visual cues such as color, texture, or lightness to measure pixel similarity, making it difficult to achieve content-aware color propagation. While some recent approaches attempt to introduce semantic information into color editing, but often lead to unnatural, global color change in color adjustments. To overcome these limitations, we present a semantic palette-guided approach for color propagation. We first extract a semantic palette from an input image. Then, we solve an edited palette by minimizing a well-designed energy function based on user edits. Finally, local edits are accurately propagated to regions that share similar semantics via the solved palette. Our approach enables efficient yet accurate pixel-level color editing and ensures that local color changes are propagated in a content-aware manner. Extensive experiments demonstrated the effectiveness of our method.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。