Graph filter banks with M-channels, maximal decimation, and perfect reconstruction

Oguzhan Teke, P. Vaidyanathan
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引用次数: 10

Abstract

Signal processing on graphs finds applications in many areas. Motivated by recent developments, this paper studies the concept of spectrum folding (aliasing) for graph signals under the downsample-then-upsample operation. In this development, we use a special eigenvector structure that is unique to the adjacency matrix of M-block cyclic matrices. We then introduce M-channel maximally decimated filter banks. Manipulating the characteristics of the aliasing effect, we construct polynomial filter banks with perfect reconstruction property. Later we describe how we can remove the eigenvector condition by using a generalized decimator. In this study graphs are assumed to be general with a possibly non-symmetric and complex adjacency matrix.
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图滤波器组与m通道,最大抽取,和完美的重建
图形上的信号处理在许多领域都有应用。受最新研究进展的启发,本文研究了下采样-上采样操作下图信号的频谱折叠(混叠)概念。在这个发展中,我们使用了一个特殊的特征向量结构,它是m块循环矩阵邻接矩阵所独有的。然后我们引入m通道最大抽取滤波器组。利用混叠效应的特点,构造了具有良好重构性能的多项式滤波器组。稍后我们将描述如何使用广义十进制数来去除特征向量条件。在本研究中,假设图具有可能非对称的复杂邻接矩阵。
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