基于Cayley变换的紧支撑二元正交小波滤波器组的构造

构造正交滤波器组,在多相域里就等价于构造仿酉矩阵,而仿酉矩阵的构造涉及到非线性方程组的求解.通过对Cayley变换的研究,把仿酉矩阵的构造转换为更易构造的仿斜厄米特矩阵,基于这种变换构造了二元紧支撑正交小波滤波器组,并给出了算例.     

中心对称仿酉矩阵的构造及二元具有线性相位正交小波滤波器组的参数化

中心对称仿酉矩阵(简记为CSPM)在线性相位的小波滤波器组的构造中起着重要的作用,本文给出偶数阶CSPM的表达式,矩阵中的元素为二元一次多项式.基于已给出的CSPM,给出具有线性相位的二元正交小波滤波器组的参数化,通过选取不同的参数可以得到的具有线性相位的正交小波滤波器组.最后给出算例.     

紧支撑二元正交小波滤波器的构造

高维小波是处理多维信号的有力工具，张量积小波有其自身的缺点．本文给出矩形域上二元正交小波滤波器的一种参数化构造算法，二元小波滤波器的这种构造方法使我们能更方便地研究非张量积的二元正交小波．最后给出算例．     

一类向量值正交小波的设计与性质

引入整数因子伸缩的向量值正交小波与向量值小波包的概念.运用仿酉向量滤波器理论和矩阵理论,给出具有整数因子伸缩的向量值正交小波存在的充要条件.提供了紧支撑向量值正交的构建算法,给出了相应的构建算例.利用时频分析方法与算子理论,刻画了一类向量值正交小波包的性质,得到了整数伸缩的向量值小波包的正交公式.     

仿酉对称矩阵的构造及对称正交多小波滤波带的参数化

仿酉矩阵在小波、多小波、框架的构造中发挥了重要的作用.本文给出仿酉对称矩阵(简记为p.s.m.)的显式构造算法,其中仿酉对称矩阵是元素为对称或反对称多项式的仿酉矩阵.基于已构造的p.s.m.和已知的正交对称多小波(简记为o.s.m.),给出o.s.m.的参数化.恰当地选择一些参数,可得到具有一些优良性质的o.s.m.,例如Armlet.最后作这一个算例,构造出一类对称的Chui-Lian Armlet滤波带.     

多小波框架的构造理论

本文给出了多小波框架的sub-QMF条件,提出了多小波框架低通滤波器的参数化设计,由正交分解和矩阵的酉扩张得到其相应的高通滤波器表示的整套多小波框架设计的参数化方法,同时针对多描述编码的需求,构造了两个长折叠对称带参数的多小波紧框架.     

二元正交多项式小波的构造

本文中,给出了一个构造二元张量积正交多项式小波的构造准则,还给出了一个二元张量积正交多项式小波的例子.     

向量值双正交小波的存在性及滤波器的构造

引进了向量值多分辨分析与向量值双正交小波的概念.讨论了向量值双正交小波的存在性.运用多分辨分析和矩阵理论,给出一类紧支撑向量值双正交小波滤波器的构造算法.最后,给出4-系数向量值双正交小波滤波器的的构造算例.     

一类二元半正交小波包的构造

本文给出伸缩矩阵行列式为2的一类二元半正交小波包的构造算法.该小波包是以频域给出的,随着用于小波包分裂的滤波器选取的不同会得到L2(R2)中形态各异的Riesz基,这样使得L2(R2)中小波基的选择更灵活.     

二维正交小波滤波器的逼近

提出了一种二维正交小波滤波器逼近的方法,采用分步优化的方法来构造小波滤波器,最后通过实验给出低通的小波滤波器.     

Shift products and factorizations of wavelet matrices

A class of so-called shift products of wavelet matrices is introduced. These products are based on circulations of columns of orthogonal banded block circulant matrices arising in applications of discrete orthogonal wavelet transforms (or paraunitary multirate filter banks) or, equivalently, on augmentations of wavelet matrices by zero columns (shifts). A special case is no shift; a product which is closely related to the Pollen product is then obtained. Known decompositions using factors formed by two blocks are described and additional conditions such that uniqueness of the factorization is guaranteed are given. Next it is shown that when nonzero shifts are used, an arbitrary wavelet matrix can be factorized into a sequence of shift products of square orthogonal matrices. Such a factorization, as well as those mentioned earlier, can be used for the parameterization and construction of wavelet matrices, including the costruction from the first row. Moreover, it is also suitable for efficient implementations of discrete orthogonal wavelet transforms and paraunitary filter banks.and Cooperative Research Centre for Sensor Signal and Information ProcessingThis author is an Overseas Postgraduate Research Scholar supported by the Australian Government.     

Non‐separable multivariate filter banks from univariate wavelets

The main purpose of this paper is the design of multivariate filter banks starting from univariate wavelet filters. The matrix completion is solved by utilizing some special Toeplitz matrices. A generalized method of rational polynomial univariate filter with preset zero points set is constructed.     

Hexagonal tight frame filter banks with idealized high-pass filters

This paper studies the construction of hexagonal tight wavelet frame filter banks which contain three “idealized” high-pass filters. These three high-pass filters are suitable spatial shifts and frequency modulations of the associated low-pass filter, and they are used by Simoncelli and Adelson in (Proc IEEE 78:652–664, 1990) for the design of hexagonal filter banks and by Riemenschneider and Shen in (Approximation Theory and Functional Analysis, pp. 133–149, Academic Press, Boston 1991; J. Approx Theory 71:18–38 1992) for the construction of 2-dimensional orthogonal filter banks. For an idealized low-pass filter, these three associated high-pass filters separate high frequency components of a hexagonal image in 3 different directions in the frequency domain. In this paper we show that an idealized tight frame, a frame generated by a tight frame filter bank containing the “idealized” high-pass filters, has at least 7 frame generators. We provide an approach to construct such tight frames based on the method by Lai and Stöckler in (Appl Comput Harmon Anal 21:324–348, 2006) to decompose non-negative trigonometric polynomials as the summations of the absolute squares of other trigonometric polynomials. In particular, we show that if the non-negative trigonometric polynomial associated with the low-pass filter p can be written as the summation of the absolute squares of other 3 or less than 3 trigonometric polynomials, then the idealized tight frame associated with p requires exact 7 frame generators. We also discuss the symmetry of frame filters. In addition, we present in this paper several examples, including that with the scaling functions to be the Courant element B ₁₁₁ and the box-spline B ₂₂₂. The tight frames constructed in this paper will have potential applications to hexagonal image processing.     

轮廓波变换原理及其构造方法

本文主要介绍轮廓波变换的基本原理,对轮廓波多方向性实现的关键技术——多维多采样率系统进行了阐述并详细说明了方向滤波器的构造过程,最后总结出了构造方向滤波器组常用的两种方法——McClellan变换法和多相位表示法,并给出了9×9参数化扇形滤波器组的构造实例.     

Dyadic Bivariate Fourier Multipliers for Multi-Wavelets in L2(R2)

The single 2 dilation orthogonal wavelet multipliers in one dimensional case and single A-dilation(where A is any expansive matrix with integer entries and|det A|=2) wavelet multipliers in high dimensional case were completely characterized by the Wutam Consortium(1998) and Z. Y. Li, et al.(2010). But there exist no more results on orthogonal multivariate wavelet matrix multipliers corresponding integer expansive dilation matrix with the absolute value of determinant not 2 in L~2(R~2). In this paper, we choose 2I2=(_0~2 _2~0)as the dilation matrix and consider the 2 I2-dilation orthogonal multivariate waveletΨ = {ψ_1, ψ_2, ψ_3},(which is called a dyadic bivariate wavelet) multipliers. We call the3 × 3 matrix-valued function A(s) = [ f_(i, j)(s)]_(3×3), where fi, jare measurable functions, a dyadic bivariate matrix Fourier wavelet multiplier if the inverse Fourier transform of A(s)( ψ_1(s), ψ_2(s), ψ_3(s)) ~T=( g_1(s), g_2(s), g_3(s))~ T is a dyadic bivariate wavelet whenever(ψ_1, ψ_2, ψ_3) is any dyadic bivariate wavelet. We give some conditions for dyadic matrix bivariate wavelet multipliers. The results extended that of Z. Y. Li and X. L.Shi(2011). As an application, we construct some useful dyadic bivariate wavelets by using dyadic Fourier matrix wavelet multipliers and use them to image denoising.     

基于正弦和余弦函数的小波滤波器的统一解析构造

首次提出用正弦函数和余弦函数解析构造任意长度的紧支集正交小波滤波系数,首先给出了对N＝2k-1时（k个参数）的解析结构,其次给出了N＝2k时正交小波滤波器的统一构造方法,此后验证了著名的Daubechies小波滤波器的构成参数,并验证了一些被广泛的使用的著名小波分析滤波器,所有这些滤波器容易用一组参数直接计算出来,小波滤波器的解析构造使得在应用中动态选择小波基变得极基容易,这一结果必将在小波理论,应用数学及模式识别等领域产生十分重要的作用。