论文标题
图像矢量化，缩放和像素艺术的几何总变化
Geometric Total Variation for Image Vectorization, Zooming and Pixel Art Depixelizing
论文作者
论文摘要
我们提出了一种原始方法，用于矢量化图像或以任意比例进行缩放。我们方法的核心依赖于几何变异模型的分辨率，因此提供了理论保证。更确切地说，它将总变异能与图像像素的每个有效三角剖分相关联。它的最小化诱导了反映图像梯度的三角形。然后，我们将此三角剖分利用为精确定位不连续性，然后可以简单地将其矢量化或缩放。这种新方法在没有任何学习阶段的任意图像上起作用。对于处理诸如像素艺术（像素艺术）的低量化图像的处理特别有吸引力，可用于将此类图像进行排化。该方法可以通过在线演示器进行评估，在线演示器可以在此处复制此处介绍的结果或上传自己的图像。
We propose an original method for vectorizing an image or zooming it at an arbitrary scale. The core of our method relies on the resolution of a geometric variational model and therefore offers theoretic guarantees. More precisely, it associates a total variation energy to every valid triangulation of the image pixels. Its minimization induces a trian-gulation that reflects image gradients. We then exploit this triangulation to precisely locate discontinuities, which can then simply be vectorized or zoomed. This new approach works on arbitrary images without any learning phase. It is particularly appealing for processing images with low quantization like pixel art and can be used for depixelizing such images. The method can be evaluated with an online demonstrator, where users can reproduce results presented here or upload their own images.
