Multi-Channel Sampling on Graphs and Its Relationship to Graph Filter Banks
Multi-Channel Sampling on Graphs and Its Relationship to Graph Filter Banks
复制标题
图的多通道采样及其与图滤波器组的关系
DOI：
10.1109/ojsp.2023.3249112
复制
发表时间：
2023
影响因子：
2.8
通讯作者：
Tanaka Yuichi
中科院分区：
文献类型：
--
作者：
Hara Junya;Tanaka Yuichi
在本文中，我们考虑了图形信号的多通道采样（MCS）。在许多应用中，我们通常会遇到超出带宽限制的全频带图信号，例如分段常数/光滑图信号和带宽限制图信号的并集。全频带图信号可以由符合不同生成模型的多个信号混合表示。这需要通过多个采样系统（即MCS）分析图形信号，而现有方法仅考虑单通道采样。我们开发了一个基于广义抽样的MCS框架。我们还提出了一种采样集选择（SSS）方法，以使所提出的MCS信号得到最好的恢复。此外，我们发现现有的图滤波器组可以被视为所提出的MCS的特殊情况。在信号恢复实验中，该方法对全频带图信号的恢复效果良好。
In this article, we consider multi-channel sampling (MCS) for graph signals. We generally encounter full-band graph signals beyond the bandlimited ones in many applications, such as piecewise constant/smooth graph signals and union of bandlimited graph signals. Full-band graph signals can be represented by a mixture of multiple signals conforming to different generation models. This requires the analysis of graph signals via multiple sampling systems, i.e., MCS, while existing approaches only consider single-channel sampling. We develop a MCS framework based on generalized sampling. We also present a sampling set selection (SSS) method for the proposed MCS so that the graph signal is best recovered. Furthermore, we reveal that existing graph filter banks can be viewed as a special case of the proposed MCS. In signal recovery experiments, the proposed method exhibits the effectiveness of recovery for full-band graph signals.