Spectral Domain Sampling of Graph Signals
arXiv:1706.05147 · doi:10.1109/TSP.2018.2839620
Abstract
Sampling methods for graph signals in the graph spectral domain are presented. Though conventional sampling of graph signals can be regarded as sampling in the graph vertex domain, it does not have the desired characteristics in regard to the graph spectral domain. With the proposed methods, the down- and upsampled graph signals inherit the frequency domain characteristics of the sampled signals defined in the time/spatial domain. The properties of the sampling effects were evaluated theoretically in comparison with those obtained with the conventional sampling method in the vertex domain. Various examples of signals on simple graphs enable precise understanding of the problem considered. Fractional sampling and Laplacian pyramid representation of graph signals are potential applications of these methods.
accepted to IEEE Transactions on Signal Processing
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- Brain Graph Super-Resolution Using Adversarial Graph Neural Network with Application to Functional Brain Connectivity