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.
|Title of host publication
|30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings
|Number of pages
|Published - 2022
|30th European Signal Processing Conference, EUSIPCO 2022 - Belgrade, Serbia
Duration: Aug 29 2022 → Sep 2 2022
|European Signal Processing Conference
|30th European Signal Processing Conference, EUSIPCO 2022
|8/29/22 → 9/2/22
Bibliographical noteFunding Information:
This work was supported by the National Science Foundation under grants 1815992 and 1816003, and by a graduate
scholarship from the Institute of Financial Services Analytics, sponsored by the University of Delaware and JP Morgan Chase & Co.
© 2022 European Signal Processing Conference, EUSIPCO. All rights reserved.
- graph signal processing
- Graph signal reconstruction
- graph signal sampling
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering