Density Aware Blue-Noise Sampling on Graphs

Daniela Dapena, Daniel L. Lau, Gonzalo R. Arce

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


Efficient sampling of graph signals is essential to graph signal processing. Recently, blue-noise was introduced as a sampling method that maximizes the separation between sampling nodes leading to high-frequency dominance patterns, and thus, to high-quality patterns. Despite the simple interpretation of the method, blue-noise sampling is restricted to approximately regular graphs. This study presents an extension of blue-noise sampling that allows the application of the method to irregular graphs. Before sampling with a blue-noise algorithm, the approach regularizes the weights of the edges such that the graph represents a regular structure. Then, the resulting pattern adapts the node's distribution to the local density of the nodes. This work also uses an approach that minimizes the strength of the high-frequency components to recover approximately bandlimited signals. The experimental results show that the proposed methods have superior performance compared to the state-of-the-art techniques.

Original languageEnglish
Title of host publication30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings
Number of pages5
ISBN (Electronic)9789082797091
StatePublished - 2022
Event30th European Signal Processing Conference, EUSIPCO 2022 - Belgrade, Serbia
Duration: Aug 29 2022Sep 2 2022

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


Conference30th European Signal Processing Conference, EUSIPCO 2022

Bibliographical note

Funding Information:
This work was supported by the National Science Foundation under grants 1815992 and 1816003, and by a graduate

Funding Information:
scholarship from the Institute of Financial Services Analytics, sponsored by the University of Delaware and JP Morgan Chase & Co.

Publisher Copyright:
© 2022 European Signal Processing Conference, EUSIPCO. All rights reserved.


  • blue-noise
  • graph signal processing
  • Graph signal reconstruction
  • graph signal sampling

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


Dive into the research topics of 'Density Aware Blue-Noise Sampling on Graphs'. Together they form a unique fingerprint.

Cite this