With the surge in the volumes and dimensions of data defined in non-Euclidean spaces, graph signal processing (GSP) techniques are emerging as important tools in our understanding of these domains [1]. A fundamental problem for GSP is to determine which nodes play the most important role; so, graph signal sampling and recovery thus become essential [2]. In general, most of the current sampling methods are based on graph spectral decompositions where the graph Fourier transform (GFT) plays a central role [2]. Although adequate in many cases, they are not applicable when the graphs are large and where spectral decompositions are computationally difficult [3]. After years of beautiful and useful theoretical insights developed in this problem, the interest has now centered on finding more efficient methods for the computation of good sampling sets. Looking to the spatial domain for inspiration, substantial research has been performed that looks at the use of spatial point processes to define stochastic sampling grids with a particular interest at point processes that generate "blue noise."

中文翻译：

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图和多图信号的蓝噪声采样：非欧域上的抖动

随着非欧空间中定义的数据量和维度的激增，图信号处理 (GSP) 技术正在成为我们理解这些领域的重要工具 [1]。GSP 的一个基本问题是确定哪些节点发挥最重要的作用；因此，图形信号采样和恢复因此变得必不可少[2]。通常，当前的大多数采样方法都基于图谱分解，其中图傅立叶变换 (GFT) 起着核心作用 [2]。尽管在许多情况下足够了，但当图很大且谱分解在计算上很困难时，它们不适用 [3]。经过多年在这个问题上发展出美丽而有用的理论见解，现在的兴趣集中在寻找更有效的方法来计算良好的采样集。