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The extended generalized Haar-Walsh transform and applications
Author(s): Yiqun Shao; Naoki Saito
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Paper Abstract

Extending computational harmonic analysis tools from the classical setting of regular lattices to the more general setting of graphs and networks is very important and much research has been done recently. Our previous Generalized Haar-Walsh Transform (GHWT) is a multiscale transform for signals on graphs, which is a generalization of the classical Haar and Walsh-Hadamard Transforms. This article proposes the extended Generalized HaarWalsh Transform (eGHWT). The eGHWT and its associated best-basis selection algorithm for graph signals will significantly improve the performance of the previous GHWT with the similar computational cost, O(N log N) where N is the number of nodes of an input graph. While the previous GHWT/best-basis algorithm seeks the most suitable orthonormal basis for a given task among more than (1.5)N possible bases, the eGHWT/best-basis algorithm can find a better one by searching through more than 0.618 · (1.84)N possible bases. This article describes the details of the eGHWT/basis-basis algorithm and demonstrates its superiority using several examples including genuine graph signals as well as conventional digital images viewed as graph signals.

Paper Details

Date Published: 9 September 2019
PDF: 17 pages
Proc. SPIE 11138, Wavelets and Sparsity XVIII, 111380C (9 September 2019); doi: 10.1117/12.2528923
Show Author Affiliations
Yiqun Shao, Univ. of California, Davis (United States)
Naoki Saito, Univ. of California, Davis (United States)


Published in SPIE Proceedings Vol. 11138:
Wavelets and Sparsity XVIII
Dimitri Van De Ville; Manos Papadakis; Yue M. Lu, Editor(s)

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