Blue-noise sampling on graphs

Alejandro Parada-Mayorga, Daniel L. Lau, Jhony H. Giraldo, Gonzalo R. Arce

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


In the area of graph signal processing, a graph is a set of nodes arbitrarily connected by weighted links; a graph signal is a set of scalar values associated with each node; and sampling is the problem of selecting an optimal subset of nodes from which a graph signal can be reconstructed. This paper proposes the use of spatial dithering on the vertex domain of the graph, as a way to conveniently find statistically good sampling sets. This is done establishing that there is a family of good sampling sets characterized on the vertex domain by a maximization of the distance between sampling nodes; in the Fourier domain, these are characterized by spectrums that are dominated by high frequencies referred to as blue-noise. The theoretical connection between blue-noise sampling on graphs and previous results in graph signal processing is also established, explaining the advantages of the proposed approach. Restricting our analysis to undirected and connected graphs, numerical tests are performed in order to compare the effectiveness of blue-noise sampling against other approaches.

Original languageEnglish
Article number8736327
Pages (from-to)554-569
Number of pages16
JournalIEEE Transactions on Signal and Information Processing over Networks
Issue number3
StatePublished - Sep 2019

Bibliographical note

Publisher Copyright:
© 2015 IEEE.


  • Blue-noise sampling
  • graph signal processing
  • sampling sets
  • signal processing on graphs

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Computer Networks and Communications


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