标题： 语义调色板引导的颜色传播 显示英文标题

标题： Semantic Palette-Guided Color Propagation

摘要： 颜色传播旨在将局部颜色编辑扩展到输入图像中的相似区域。 传统方法通常依赖于颜色、纹理或亮度等低级视觉线索来衡量像素相似性，这使得实现内容感知的颜色传播变得困难。 尽管一些近期方法尝试将语义信息引入颜色编辑中，但往往会导致颜色调整中的不自然的全局颜色变化。 为克服这些限制，我们提出了一种基于语义调色板引导的颜色传播方法。 首先，我们从输入图像中提取语义调色板。 然后，通过最小化一个精心设计的能量函数（基于用户编辑），解决编辑后的调色板问题。 最后，通过解决的调色板，将局部编辑准确地传播到具有相似语义的区域。 我们的方法实现了高效的像素级颜色编辑，并确保局部颜色变化以内容感知的方式传播。 广泛的实验验证了我们方法的有效性。 显示英文摘要

摘要： 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.