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New schemes to recover signals defined in the nodes of a graph are proposed. Our focus is on reconstructing bandlimited graph signals, which are signals that admit a sparse representation in a frequency domain related to the structure of the graph. Most existing formulations focus on estimating an unknown graph signal by observing its value on a subset of nodes. By contrast, in this paper, we study...
A scheme to sample bandlimited graph signals in the presence of noise is analyzed. Samples are aggregated at a single node by successive applications of the so-called graph-shift operator that encodes the local structure of the underlying graph. In contrast to the noiseless case, when noise is present the choice of the sampling node and the local sample-selection scheme plays a major role in determining...
Schemes to reconstruct signals defined in the nodes of a graph are proposed. Our focus is on reconstructing bandlimited graph signals, which are signals that admit a sparse representation in a frequency domain related to the structure of the graph. The schemes, which are designed within the framework of linear shift-invariant graph filters, consider that the signal is injected at a single seeding...
A new scheme to sample bandlimited graph signals is proposed. The signals are defined in the nodes of a graph and admit a sparse representation in a frequency domain related to the structure of the graph, which is captured by the so- called graph-shift operator. Most of the existing works focused on using the value of the signal observed at a subset of nodes to recover the signal in the entire graph...
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