Semantic Palette-Guided Color Propagation
This work addresses the challenge of achieving natural, content-aware color adjustments in image editing, offering an incremental improvement over existing methods that rely on low-level cues.
The paper tackled the problem of content-aware color propagation in image editing by introducing a semantic palette-guided approach, which accurately propagates local color edits to semantically similar regions, as demonstrated through extensive experiments.
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.