论文标题
图表上的多通道采样及其与图形过滤库的关系
Multi-channel Sampling on Graphs and Its Relationship to Graph Filter Banks
论文作者
论文摘要
在本文中，我们考虑图形信号的多通道采样（MC）。我们通常会遇到超越频段限制的全频段图信号，例如分段恒定/平滑和带有频带的图形信号的结合。全频段图信号可以由符合不同生成模型的多个信号的混合物表示。这需要通过多个采样系统（即MC）对图形信号进行分析，而现有方法仅考虑单通道采样。我们基于广义采样开发了MCS框架。我们还为提出的MC提供了一个采样集选择（SSS）方法，以便最好恢复图形信号。此外，我们透露，现有的图形滤波器可以看作是拟议的MC的特殊情况。在信号恢复实验中，提出的方法表现出对全带图信号的恢复有效性。
In this paper, we consider multi-channel sampling (MCS) for graph signals. We generally encounter full-band graph signals beyond the bandlimited one in many applications, such as piecewise constant/smooth 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.
