Nonsubsampled Graph Filter Banks: Theory and Distributed Algorithms

In this paper, we consider nonsubsampled graph filter banks (NSGFBs) to process data on a sparse graph. The analysis filter banks of NSGFBs have small bandwidth, pass/block the normalized constant signal, and have stability on ℓ 2 . Given an analysis filter bank with small bandwidth, we introduce al...

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Veröffentlicht in:IEEE transactions on signal processing Jg. 67; H. 15; S. 3938 - 3953
Hauptverfasser: Jiang, Junzheng, Cheng, Cheng, Sun, Qiyu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.08.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN:1053-587X, 1941-0476
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Zusammenfassung:In this paper, we consider nonsubsampled graph filter banks (NSGFBs) to process data on a sparse graph. The analysis filter banks of NSGFBs have small bandwidth, pass/block the normalized constant signal, and have stability on ℓ 2 . Given an analysis filter bank with small bandwidth, we introduce algebraic and optimization methods to construct well-localized synthesis filter banks such that the corresponding NSGFBs provide a perfect signal reconstruction in the noiseless setting. We also prove that the proposed NSGFBs can control the resonance effect in the presence of bounded noise and they can limit the influence of shot noise primarily to a small neighborhood near its location on the graph. We later introduce an iterative algorithm to implement the proposed NSGFBs in a distributed manner, and develop an NSGFB-based denoising technique which is demonstrated to have satisfactory performance on noise suppression.
Bibliographie:ObjectType-Article-1
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2019.2922160