Design of Graph Signal Sampling Matrices for Arbitrary Signal Subspaces
Design of Graph Signal Sampling Matrices for Arbitrary Signal Subspaces
复制标题
DOI：
10.1109/icassp39728.2021.9414240
复制
发表时间：
2021-06
期刊：
影响因子：
--
通讯作者：
Junya Hara;Koki Yamada;Shunsuke Ono;Yuichi Tanaka
中科院分区：
文献类型：
--
作者：
Junya Hara;Koki Yamada;Shunsuke Ono;Yuichi Tanaka
我们提出了一种图信号采样矩阵的设计方法，可以保证任意图信号子空间的完美恢复。当信号子空间是已知的，完美的重建总是可能的，从样本与适当设计的采样矩阵。然而，目前大多数图信号采样方法都是基于带限假设来设计采样矩阵，有时会违反其他信号模型的完美重构条件。在本文中，我们制定了一个优化问题的采样矩阵的设计，保证完美的恢复，由于标准信号的广义采样框架。在各种信号模型的实验中，我们的采样矩阵在无噪声和有噪声的情况下都具有更好的重建精度。
We propose a design method of sampling matrices for graph signals that guarantees perfect recovery for arbitrary graph signal subspaces. When the signal subspace is known, perfect reconstruction is always possible from the samples with an appropriately designed sampling matrix. However, most graph signal sampling methods so far design sampling matrices based on the bandlimited assumption and sometimes violates the perfect reconstruction condition for the other signal models. In this paper, we formulate an optimization problem for the design of the sampling matrix that guarantees perfect recovery, thanks to a generalized sampling framework for standard signals. In experiments with various signal models, our sampling matrix presents better reconstruction accuracy both for noiseless and noisy situations.