We propose an iterative algorithm to interpolate graph signals from only a partial set of samples. Our method is derived from the well known Papoulis-Gerchberg algorithm by considering the optimal value of a constant involved in the iteration step. Compared with existing graph signal reconstruction algorithms, the proposed method achieves similar or better performance both in terms of convergence rate and computational efficiency
International audienceWe consider the problem of signal interpolation on graphs, i.e. recovering one...
We propose a sampling theory for signals that are supported on either directed or undirected graphs....
In this paper, we extend the Nyquist-Shannon theory of sampling to signals defined on arbitrary grap...
We propose an iterative algorithm to interpolate graph signals from only a partial set of samples. O...
In applications such as social, energy, transportation, sensor, and neuronal networks, big data natu...
In this paper, we present two localized graph filtering based meth-ods for interpolating graph signa...
With the explosive growth of information and communication, data is being generated at an unpreceden...
Signal processing on graph is attracting more and more attention. For a graph signal in the low-freq...
The emerging eld of signal processing on graph plays a more and more impor-tant role in processing s...
In this paper the focus is on sampling and reconstruction of signals supported on nodes of arbitrary...
New schemes to recover signals defined in the nodes of a graph are proposed. Our focus is on reconst...
Bandlimited graph signals on an unweighted graph can be reconstructed by its local measurement, whic...
The rapid development of signal processing on graphs provides a new perspective for processing large...
Multiscale analysis of signals on graphs often involves the downsampling of a graph. In this paper, ...
This thesis consists of two parts in both data science and signal processing over graphs. In the fir...
International audienceWe consider the problem of signal interpolation on graphs, i.e. recovering one...
We propose a sampling theory for signals that are supported on either directed or undirected graphs....
In this paper, we extend the Nyquist-Shannon theory of sampling to signals defined on arbitrary grap...
We propose an iterative algorithm to interpolate graph signals from only a partial set of samples. O...
In applications such as social, energy, transportation, sensor, and neuronal networks, big data natu...
In this paper, we present two localized graph filtering based meth-ods for interpolating graph signa...
With the explosive growth of information and communication, data is being generated at an unpreceden...
Signal processing on graph is attracting more and more attention. For a graph signal in the low-freq...
The emerging eld of signal processing on graph plays a more and more impor-tant role in processing s...
In this paper the focus is on sampling and reconstruction of signals supported on nodes of arbitrary...
New schemes to recover signals defined in the nodes of a graph are proposed. Our focus is on reconst...
Bandlimited graph signals on an unweighted graph can be reconstructed by its local measurement, whic...
The rapid development of signal processing on graphs provides a new perspective for processing large...
Multiscale analysis of signals on graphs often involves the downsampling of a graph. In this paper, ...
This thesis consists of two parts in both data science and signal processing over graphs. In the fir...
International audienceWe consider the problem of signal interpolation on graphs, i.e. recovering one...
We propose a sampling theory for signals that are supported on either directed or undirected graphs....
In this paper, we extend the Nyquist-Shannon theory of sampling to signals defined on arbitrary grap...