基于RGB空间几何的数字绘画图层分解
Decomposing Digital Paintings into Layers via RGB-space Geometry
摘要：
在数字绘画软件中，图层组织绘画。然而，层并没有明确地表示，传输，或与最终的数字绘画一起发布。我们提出了一种技术来分解成层的数字绘画。在我们的分解中，每一层都代表一层单一颜色的油漆，在整个图像中应用不同的不透明度。我们的分解是基于绘画的RGB空间几何。在RGB空间中，由于标准Porter-Duff“over”像素合成操作的线性性质，揭示了几何结构。RGB空间中的像素的凸包的顶点表示油漆颜色。用户可以选择在凸包上执行的简化程度以及颜色的层顺序。我们解决了一个约束优化问题，找到最大的半透明，空间相干不透明度的每一层，使层的组成再现原始图像。我们证明了重新编辑所得到的分解的效用。
英文摘要：
In digital painting software, layers organize paintings. However, layers are not explicitly represented, transmitted, or published with the final digital painting. We propose a technique to decompose a digital painting into layers. In our decomposition, each layer represents a coat of paint of a single paint color applied with varying opacity throughout the image. Our decomposition is based on the painting's RGB-space geometry. In RGB-space, a geometric structure is revealed due to the linear nature of the standard Porter-Duff "over" pixel compositing operation. The vertices of the convex hull of pixels in RGB-space suggest paint colors. Users choose the degree of simplification to perform on the convex hull, as well as a layer order for the colors. We solve a constrained optimization problem to find maximally translucent, spatially coherent opacity for each layer, such that the composition of the layers reproduces the original image. We demonstrate the utility of the resulting decompositions for re-editing.
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