Continuous-time signals are well known for not being perfectly localized in both time and frequency domains. Conversely, a signal defined over the vertices of a graph can be perfectly localized in both vertex and frequency domains. We derive the conditions ensuring the validity of this property and then, building on this theory, we provide the conditions for perfect reconstruction of a graph signal from its samples. Next, we provide a finite step algorithm for the reconstruction of a band-limited signal from its samples and then we show the effect of sampling a non perfectly band-limited signal and show how to select the bandwidth that minimizes the mean square reconstruction error
New schemes to recover signals defined in the nodes of a graph are proposed. Our focus is on reconst...
In many applications of current interest, the observations are represented as a signal defined over ...
Recovery of a graph signal from samples has many important applications in signal processing over ne...
Continuous-time signals are well known for not being perfectly localized in both time and frequency ...
In this paper, we extend the Nyquist-Shannon theory of sampling to signals defined on arbitrary grap...
Multiscale analysis of signals on graphs often involves the downsampling of a graph. In this paper, ...
Abstract—We propose a sampling theory for signals that are supported on either directed or undirecte...
International audienceGiven a weighted undirected graph, this paper focuses on the sampling problem ...
We propose a sampling theory for signals that are supported on either directed or undirected graphs....
Signal processing on graph is attracting more and more attention. For a graph signal in the low-freq...
In applications such as social, energy, transportation, sensor, and neuronal networks, big data natu...
In this paper the focus is on sampling and reconstruction of signals supported on nodes of arbitrary...
The emerging eld of signal processing on graph plays a more and more impor-tant role in processing s...
With the explosive growth of information and communication, data is being generated at an unpreceden...
Schemes to reconstruct signals defined in the nodes of a graph are proposed. Our focus is on reconst...
New schemes to recover signals defined in the nodes of a graph are proposed. Our focus is on reconst...
In many applications of current interest, the observations are represented as a signal defined over ...
Recovery of a graph signal from samples has many important applications in signal processing over ne...
Continuous-time signals are well known for not being perfectly localized in both time and frequency ...
In this paper, we extend the Nyquist-Shannon theory of sampling to signals defined on arbitrary grap...
Multiscale analysis of signals on graphs often involves the downsampling of a graph. In this paper, ...
Abstract—We propose a sampling theory for signals that are supported on either directed or undirecte...
International audienceGiven a weighted undirected graph, this paper focuses on the sampling problem ...
We propose a sampling theory for signals that are supported on either directed or undirected graphs....
Signal processing on graph is attracting more and more attention. For a graph signal in the low-freq...
In applications such as social, energy, transportation, sensor, and neuronal networks, big data natu...
In this paper the focus is on sampling and reconstruction of signals supported on nodes of arbitrary...
The emerging eld of signal processing on graph plays a more and more impor-tant role in processing s...
With the explosive growth of information and communication, data is being generated at an unpreceden...
Schemes to reconstruct signals defined in the nodes of a graph are proposed. Our focus is on reconst...
New schemes to recover signals defined in the nodes of a graph are proposed. Our focus is on reconst...
In many applications of current interest, the observations are represented as a signal defined over ...
Recovery of a graph signal from samples has many important applications in signal processing over ne...