基于多尺度Wiener滤波器的分形噪声滤波

针对淹没在1/f噪声中的有用信号恢复问题,本文提出了一套基于双正交小波变换与Wiener滤波的多尺度滤波算法,并设计出多尺度Wiener滤波器.首先,利用双正交小波变换将带有1/f噪声的信号分解成多尺度的子带信号,通过小波变换对1/f噪声的白化作用,消除了1/f噪声的非平稳性、自相似性和长程相关性.其次,在小波域内,利用Wiener滤波,实现了噪声和有用信号的分离,估计出了各子带中的有用信号.最后,利用双正交小波的精确重构性,较好地恢复出淹没在1/f噪声中的有用信号.仿真实验表明,该滤波器能有效的抑制分形噪声,显著地提高信噪比.     

小波分析在光纤陀螺分形噪声模拟中的应用

光纤陀螺随机误差的功率谱密度分别与频率的γ次方成反比,这类随机过程统称为1/fγ分形噪声,研究生成这类信号的方法对分析光纤陀螺的输出信号具有重要意义。分形噪声具有非平稳性、长程相关性、自相似性及1/fγ谱密度的特性,小波变换的多分辨分析是研究1/fγ噪声的良好工具。通过对高斯白噪声进行小波变换,再结合1/fγ噪声的方差特性,找到了满足1/fγ信号生成定理的各尺度正交小波系数,最后采用正交小波逆变换模拟出分形噪声,此方法可以产生任意噪声强度σ2、任意谱参数γ的1/fγ噪声。     

工程数学

()17连.2 01040017单透镜成象系统的分数维傅立叶变换分析/杜良(华东删白工业学l晓)11应用激光.一2000,20(5)一201一204在非涅耳衍射与分数维傅立叶变换关系的理论基础上,给出了这一关系的另一表示方式,以此理论和透镜的分数维傅立叶变换特性分析了放大率大于1和小于1的单透镜成象系统.结果表明,作为非涅耳衍射的分数维傅立叶变换可以与透镜的分数维傅立叶变换组成分数维傅立叶变换群,两次分数维傅立叶变换完成了一次成象过程.图5参5(许)573基于积分小波变换研究了1/f信号的表示,给出了用小波逆变换产生1/f信号的基本条件.为了进一步说明这…     

1/f分形噪声的一种多尺度Kalman滤波方法

针对淹没在1／f分形噪声中的有用信号恢复问题，提出了一种基于小波变换与Kalman滤波的多尺度滤波算法。首先将带有1／f分形噪声的信号分解成多尺度的子带信号，通过小波变换对1／f分形噪声的白化作用，消除了1／f分形噪声的自相似性和长程相关性。然后在小波域内，利用Kalman滤波实现了噪声和有用信号的分离，估计出了各子带中的有用信号。最后进行小波重构，较好地恢复出淹没在1／f分形噪声中的有用信号。仿真实验表明，使用多尺度Kalman滤波器能有效地抑制分形噪声，显著地提高了信噪比。     

1/f类分形信号的最小二乘法参数估计

根据含噪1/f类分形信号的小波变换系数方差随尺度变化的特点,该文提出了一种估计1/f类分形信号参数的新方法,即对其小波系数方差进行简单的变换,使1/f类分形信号参数估计满足最小二乘法参数估计的条件。仿真实验结果表明,该方法可以有效地从加性白噪声背景下估计出1/f类分形信号的,2等参数,从而使1/f类分形信号与加性白噪声分离。     

间歇混沌合成1/f噪声的相关特性分析

本文利用非线性随机微分方程来合成间歇混沌信号,针对该信号表现出的1/f噪声特征,在不同消失矩的小波基下进行相关特性分析.仿真结果发现,在功率谱的中间频段内,该信号的功率谱密度表现出典型的1/f噪声特性,其小波变换系数方差与相应的小波尺度呈对数线性关系;且在该频段内,部分尺度下该间歇性信号的小波变换系数的相关性随小波基的消失矩的增大而减小,在另一部分尺度下该相关性则随着消失矩的增大而增大.实验结果表明,随小波消失矩的增大,并非在所有尺度下小波变换对该间歇性信号均具有去相关作用.论文讨论了小波变换系数的方差和尺度的关系,详细分析了小波变换系数的相关性随小波消失矩的变化趋势.     

分形噪声中的信号检测

本文讨论了在1/f类分形噪声中的信号检测问题,利用小波变换对1/f噪声的近似白化作用,来消除1/f噪声间的相关性。文中给出了白化滤波器的传递函数,信号检测的判决规则和接收系统结构;分析了系统的接收性能;最后给出了仿真实验结果。     

基于小波变换的压电陀螺类噪声滤波器设计

1/fγ类随机噪声是影响压电陀螺精度的主要因素之一,其中随机信号包括白噪声和分形噪声.由于分形噪声具有长期相关性和自仿射性,采用传统的低通滤波方法难以达到有效的滤波效果.本文利用分形噪声在小波变换域的特性采用小波变换域参数估计的方法获得噪声参数,通过小波白化的方法消除分形噪声的长期相关性和自仿射性,最后应用小波软阈值的滤波方法去噪.经过对某型号压电陀螺信号进行滤波实验,结果表明这种基于小波分析的滤波方法是有效的.     

基于小波的2-D分形布朗运动分析与合成

1/f分形随机过程广泛地存在于各种自然现象和社会现象中,日益成为信号检测与估计、信号处理及图像处理的研究热点.分形布朗运动是模拟此类信号的很好的数学模型.小波因其所具有的多尺度分析能力成为分析分形信号的有力工具.本文分析了二维分形布朗运动经小波变换后各尺度间小波系数相关结构的特性,提出了一种合成二维分形布朗运动的算法,并展示了其在和谐图案生成上的应用.     

非平稳分形随机信号波形估计的最优门限方法

本文用基于最小均方误差准则的最优门限方法估计叠加高斯白噪声的分形布朗运动,并给出其离散小波变换分解级数确定方法.与多尺度维纳滤波相比,本方法不需估计1/f类分形信号的方差,且其离散小波变换分解级数可预先确定,因此有着更好的实用性和可操作性.     

SMOOTHING OF 1/F SIGNAL WITH ORTHOGONAL MULTIWAVELET

Based on the orthogonal multiwavelet model of 1/f signals, smoothing fractal signals from white Gaussian noise with multiwavelet filter is proposed. The proposed multiwavelet method is very simple and easy to realize. Compared with Wornell＇s single wavelet method, the new method has r filtering factors at each scale and has higher filtering speed, where r is the multiplicity of multiwavelet. Also due to the advantages of multiwavelet, the multiwavelet method performs better than that of Wornell＇s. Simulation results verify, the analysis, and Wornell＇s method is the special case of our method when r = 1.     

正交插值多子波理论和构造

该文根据多子波采样定理,构造了正交插值多子波,从而可直接用信号的采样值作为初始值,使离散多子波变换的预滤波算子简化为单位算子。     

Classification brain MR images through a fuzzy multiwavelets based GMM and probabilistic neural networks

In this paper, a new approach for classification of brain tissues into White Matter, Gray Matter, Cerebral Spinal Fluid, Glial Matter, Connective and MS lesion in multiple sclerosis is introduced. This work considers fuzzy multiwavelets, Gaussian Mixture Model (GMM) and Weighted Probabilistic Neural Networks (WPNN) for the classification of the brain tissues. Multiwavelet packet transformation is employed on brain MR images. Since multiwavelet packet transformation yields larger number of subbands compared to multiwavelet and wavelet transformations, we have proposed a fuzzy-set based theory for selection of the subbands. In contrast to the standard method of subband selection, guided by the criteria of signal energy, our method is based on the discriminatory features from the multiwavelet packet transformation coefficients. Singular values are then computed from the selected subbands. The singular values of lower magnitudes are truncated for effective classification of brain tissues in the presence of noise. Probability density functions of the remaining singular values are modeled as GMM. Model parameters are estimated using stochastic EM (SEM). They are used as features for the classification. The classification is carried out using WPNN. Experiments have been carried out using the data sets composed of three modalities of brain MR images, namely T1 and T2 relaxation times and proton density weighted MR images. Experimental results prove that the proposed approach gives better classification rate at various noise levels compared to existing approaches.     

Multiwavelet bases with extra approximation properties

This paper highlights the differences between traditional wavelet and multiwavelet bases with equal approximation order. Because multiwavelet bases normally lack important properties that traditional wavelet bases (of equal approximation order) possess, the associated discrete multiwavelet transform is less useful for signal processing unless it is preceded by a preprocessing step (prefiltering). This paper examines the properties and design of orthogonal multiwavelet bases, with approximation order >1 that possess those properties that are normally absent. For these “balanced” bases (so named by Lebrun and Vetterli (see Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), Munich, Germany, vol.3, p.2473-76, 1997 and ibid., vol.46, p.1119-24, 1998)), prefiltering can be avoided. By reorganizing the multiwavelet filter bank, the development in this paper draws from results regarding the approximation order of M-band wavelet bases. The main result thereby obtained is a characterization of balanced multiwavelet bases in terms of the divisibility of certain transfer functions by powers of (z^-2r-1)/(z^-1-1). For traditional wavelets (r=1), this specializes to the usual factor (z+1) ^K     

Balanced multiwavelet bases based on symmetric FIR filters

This paper describes a basic difference between multiwavelets and scalar wavelets that explains, without using zero moment properties, why certain complications arise in the implementation of discrete multiwavelet transforms. Assuming we wish to avoid the use of prefilters in implementing the discrete multiwavelet transform, it is suggested that the behavior of the iterated filter bank associated with a multiwavelet basis of multiplicity r is more fully revealed by an expanded set of r² scaling functions φ_i,j. This paper also introduces new K-balanced orthogonal multiwavelet bases based on symmetric FIR filters. The nonlinear design equations arising in this work are solved using the Grobner basis. The minimal-length K-balanced multiwavelet bases based on even-length symmetric FIR filters are better behaved than those based on odd-length symmetric FIR filters, as illustrated by special relations they satisfy and by examples constructed     

具有无边界失真的多小波

多小波是近几年小波理论研究的一个重要方向。该文综述了多小波的重要性质,利用正交性和对称性构造了一个支集在[0,1]上具有精确重构特性和二阶逼近性的多小波,其最大特点是无边界失真效应;经平衡后,有更好的低通和高通特性,不用预滤波。实验结果表明重构效果比单小波好。     

基于不同预处理方法的多小波暂态信号去噪

在介绍多小波基本理论的基础上,探讨了多小波的不同预处理方法并对多小波滤波器响应产生的影响进行了比较.通过对噪声信号的多小波变换分析,设计基于多小波变换的去噪方法.最后通过大量的仿真工作,对不同预处理方法的多小波与传统小波的电力系统故障暂态信号去噪效果进行了深入分析,结果表明:预处理方法的选择是影响多小波去噪效果的关键因素,若选择合适的预处理方法,利用多小波对暂态信号进行去噪,可以获得比传统小波更好的去噪效果.     

Zero-crossings of a wavelet transform

The completeness, stability, and application to pattern recognition of a multiscale representation based on zero-crossings is discussed. An alternative projection algorithm is described that reconstructs a signal from a zero-crossing representation, which is stabilized by keeping the value of the wavelet transform integral between each pair of consecutive zero-crossings. The reconstruction algorithm has a fast convergence and each iteration requires O( N log² (N)) computation for a signal of N samples. The zero-crossings of a wavelet transform define a representation which is particularly well adapted for solving pattern recognition problems. As an example, the implementation and results of a coarse-to-fine stereo-matching algorithm are described     

A general approach for analysis and application of discretemultiwavelet transforms

This paper proposes a general paradigm for the analysis and application of discrete multiwavelet transforms, particularly to image compression. First, we establish the concept of an equivalent scalar (wavelet) filter bank system in which we present an equivalent and sufficient representation of a multiwavelet system of multiplicity r in terms of a set of r equivalent scalar filter banks. This relationship motivates a new measure called the good multifilter properties (GMPs), which define the desirable filter characteristics of the equivalent scalar filters. We then relate the notion of GMPs directly to the matrix filters as necessary eigenvector properties for the refinement masks of a given multiwavelet system. Second, we propose a generalized, efficient, and nonredundant framework for multiwavelet initialization by designing appropriate preanalysis and post-synthesis multirate filtering techniques. Finally, our simulations verified that both orthogonal and biorthogonal multiwavelets that possess GMPs and employ the proposed initialization technique can perform better than the popular scalar wavelets such as Daubechies'D8 wavelet and the D(9/7) wavelet, and some of these multiwavelets achieved this with lower computational complexity