基于小波萎缩法的非线性自适应数字滤波器的设计

本文中,将小波萎缩法应用于非线性自适应数字滤波器的设计,介绍利用基于离散正交小波变换的小波萎缩迭代滤波方法,设计出非线性自适应小波萎缩滤波器的模型.文中给出了非线性自适应小波萎缩滤波器的一个消噪实例,证实了非线性自适应小波萎缩滤波器鲁棒性好的特性.     

基于遗传算法的OBSA多小波滤波器的优化设计

OBSA多小波对前置滤波器选取具有任意性.该文提出了一种基于遗传算法的OBSA多小波滤波器优化设计方法.在保证多小波滤波器具有全方位平衡性、对称和反对称性、双正交性和高阶消失矩特性的前提下,在满足一定约束的条件下,根据图像变换的不同目的,设计自适应度函数,对不同的图像自适应地得到一组不同的OBSA多小波滤波器组.若图像变换的目的是进行图像压缩,据此设计自适应度函数.实验结果表明,利用此滤波器进行图像压缩,可提高图像压缩比.     

一种级联混合小波神经网络盲均衡算法

针对严重非线性失真信道,提出了一种级联混合小波神经网络自适应盲均衡器．这种均衡器在小波网 络输入层之前级联一个横向滤波器,横向滤波器的节点输出作为小波网络的输入．利用常数模代价函数分别获得横 向滤波器和小波网络的梯度信息,将两个梯度信息进行加权融合处理,可以得到混合小波网络参数调整的梯度信 息．级联混合小波网络盲均衡器实现了对非凸性误差性能曲面的线性和非线性寻优的组合．普通电话信道和非线性 信道条件下的仿真结果表明,级联混合小波网络盲均衡与前馈网络盲均衡以及传统小波网络盲均衡相比较,具有更 好的均衡性能．     

偶数长有理数对称紧支双正交小波滤波器设计

基于Daubechies紧支集小波构造方法所得到的小波滤波器多为非线性相位和无理数系数。非线性相位滤波器在图像处理过程中容易引起失真,而无理系数滤波器给小波变换的应用尤其是在算法的硬件实现方面带来不便。借助完全重构滤波器思想,进行了具有线性相位的双正交小波滤波器设计,同时通过添加消失矩特性条件,归纳推导出偶数长有理系数双正交对称紧支集小波滤波器的构造方法。以长度为8-4双正交小波设计为例,对设计方程中出现的自由变量的取值范围进行了讨论,得到了8-4有理系数双正交对称小波滤波器。     

基于小波变换和模糊中值滤波的图像边缘检测

小波变换是近年来兴起的信号处理技术。它具有良好的局部化分析特性和多分辨率分析特性,非常适合于图像处理。实际图像中常常含有噪声,噪声在小波变换中会产生大量的奇异点。中值滤波器是一种非线性滤波器,具有良好的边缘保持特性。该文提出了结合小波变换、中值滤波器和多分辨率分析的边缘检测方法,给出了一种自适应选择模糊中值滤波器因子的方法。实验结果表明,该方法是有效的。     

混响环境下的宽带波束形成语音增强方法

结合小波滤波器组理论和自适应波束形成技术,提出了一种基于宽带波束形成的麦克风阵列语音增强方法。该方法利用小波分析滤波器组将含噪语音信号变换到小波域;进行小波域阵列自适应波束形成;通过小波综合滤波器组重构增强后的语音信号。计算机仿真实验验证了该方法的有效性。     

一种改进的基于贝叶斯阈值萎缩去噪算法

基于对小波分析理论的深入学习,本文对小波阈值萎缩去噪方法的关键环节进行了研究,重点研究了小波贝叶斯阈值萎缩去噪方法在图像去噪中的应用.本文在分析小波系数的分布特点的基础上,基于空间自适应阈值的思想提出了一种新的自适应贝叶斯阈值萎缩去噪算法,仿真实验效果良好.     

基于遗传算法的OBSA多小波预滤波器

OBSA多小波对前置滤波器选取具有任意性。论文提出了一种基于遗传算法的OBSA多小波预滤波器构造方法。根据图像分解和重构的不同目的选择适应度函数,在适当的约束条件下,实现图像多小波变换的自适应预滤波。若图像多小波变换的目的是进行图像压缩,据此选择适应度函数,通过遗传算法方法,对不同的图像,自适应地得到前置滤波器,实验结果表明,利用此前置滤波器进行图像压缩,可以提高图像压缩比。若图像变换的目的是进行图像放大,通过文中介绍的方法,也可自适应地得到前置滤波器,放大后的图像质量较其它方法有明显的提高。     

基于正交小波变换的自适应语音消噪改进方法

提出一种基于正交小波变换的自适应语音消噪改进方法，这种方法可以提高自适应语音消噪过程的收敛速率．正交小波变换在自适应滤波中起到白化的作用，使自适应滤波器的输入正交化．通过正交小波分解，自适应滤波器中的信号能量降低，输入自相关矩阵的动态范围减小，特征值分布更加集中，从而使收敛速率加快．     

小波自适应滤波器在DCG去噪中的应用

设计一种新的小波自适应滤波器。首先对原始信号进行小波分解,然后去除含噪多的小波系数,将重构后的数据作为滤波器的原始输入信号,而将含噪多的小波系数作为滤波器的参考输入信号。实验结果表明,该算法信噪比较高,去噪效果较好。此外,该算法的运算量小,很适合动态心电信号这种大量数据的运算。     

一种具有自适应阈值的小波收缩去噪方法

小波收缩是一种非线性小波变换,这种算法的关键问题在于收缩算法中的收缩阈值和收缩函数(规则)。自适应理论是现代信号处理中强有力的工具。该文将自适应理论和传统的小波收缩算法相结合,提出了基于图像奇异特性自适应小波收缩"去噪"算法。该算法根据高频子带小波系数的均方根来确定最佳小波收缩阈值。阐明了最佳阈值与图像本身特性之间的关系。实验表明,该算法比一般软、硬阈值的小波收缩算法有更好的"去噪"效果,既克服硬阈值函数所产生的人为的噪声点和数学上不易处理等缺点,又避免了软阈值算法所带来的边缘模糊。从而进一步提高了图像的峰值信噪比,改善图像质量。     

Diffusion-Inspired Shrinkage Functions and Stability Results for Wavelet Denoising

We study the connections between discrete one-dimensional schemes for nonlinear diffusion and shift-invariant Haar wavelet shrinkage. We show that one step of a (stabilised) explicit discretisation of nonlinear diffusion can be expressed in terms of wavelet shrinkage on a single spatial level. This equivalence allows a fruitful exchange of ideas between the two fields. In this paper we derive new wavelet shrinkage functions from existing diffusivity functions, and identify some previously used shrinkage functions as corresponding to well known diffusivities. We demonstrate experimentally that some of the diffusion-inspired shrinkage functions are among the best for translation-invariant multiscale wavelet denoising. Moreover, by transferring stability notions from diffusion filtering to wavelet shrinkage, we derive conditions on the shrinkage function that ensure that shift invariant single-level Haar wavelet shrinkage is maximum–minimum stable, monotonicity preserving, and variation diminishing.First online version published in June, 2005     

小波域中的自适应模糊阈值图像去噪

对小波阈值去噪中的常用阈值和阈值函数进行分析,提出一种自适应的模糊阈值去噪算法,该算法在BayesShrink阈值基础上,通过增加一个修正因子,并结合模糊理论,自适应地对图像进行模糊阈值函数处理。实验表明该算法与BayesSbrink软阈值函数去噪算法相比,去噪后图像的峰值信噪比PSNR和最小均方误差MSE均有所提高,并且图像也更清晰,具有较好的去噪效果。     

基于图像局域特性的小波收缩去噪算法

传统的小波收缩去噪算法采用单一的阈值,它没有考虑到小波系数的类聚性,图像中重要小波系数类聚的局部具有重要的奇异特性,应降低阈值以保留图像的边缘;反之含有不重要小波系数的局部应提高阈值以消除更多的噪声,因此该文提出了一种基于图像局域特性的小波收缩自适应阈值去噪算法,这种算法根据图像局部的奇异性大小,选择适当的阈值进行去噪。实验结果表明,相对于传统的单一阈值去噪算法来说,新的算法可使滤波后图像的峰值信噪比有所提高,在一定程度上克服了单一阈值去噪算法无法滤除高质量图像中噪声的缺陷。     

一种改进的小波域贝叶斯图像去噪方法

基于对小波分析理论的深入学习,本文对小波测值萎缩去噪方法的关键环节进行了研究。本文在分析小波系数的分布特点的基础上,根据贝叶斯估计理论,得到贝叶斯收缩阈值,采用软阈值收缩去噪,行基于空间自适应例值和边缘检测的思想改进了一种新的自适应贝叶斯阈值萎缩去噪算法,仿真实验效果良好。     

基于小波收缩与非线性扩散的去噪算法

分析小波收缩与非线性扩散之间的内在关系并证明两者的等价性。根据等价性的特点构造新的扩散方程,提出一种基于改进的非线性扩散与二维小波收缩的混合图像去噪算法。实验结果表明,与其他去噪方法相比,该算法在计算复杂性和去噪效果方面的综合性能 较优。     

A modified adaptive IIR filter design via wavelet networks based on Lyapunov stability theory

In this paper, we present a wavelet network IIR filtering system satisfying asymptotic stability in the sense of Lyapunov unlike many other gradient descent algorithms based adaptive filtering systems. The proposed system also carries the advantages of the time-frequency specific properties of wavelet networks embedded into the proposed filter dynamics. Two experiments for system identification problems corresponding to the infinite impulse response filter design are proposed. The results verified that the proposed wavelet network infinite impulse response adaptive filtering system not only performs better than gradient descent based algorithms but also performs as good as other stability theory based optimization algorithms.     

高阶各向异性扩散小波收缩图像降噪算法

证明一种高阶各向异性扩散与小波收缩的等价性,并根据等价性利用高阶各向异性扩散与小波收缩的优势,提出高阶各向异性扩散小波收缩降噪算法。该算法在低频部分采用经典的非线性扩散方法进行扩散,在高频部分采用高阶各向异性扩散方法进行小波收缩。实验结果表明,高阶各向异性扩散小波收缩算法的计算复杂性介于高阶各向异性扩散与小波收缩算法之间,降噪能力高于这2种方法。     

Nonlinear Adaptive Wavelet Control Using Constructive Wavelet Networks

In this paper, an adaptive wavelet-network-based control approach is proposed for highly nonlinear uncertain dynamical systems. Wavelet network, as a kind of universal approximator, has two novel properties-orthonormality and multiresolution. The orthonormal property ensures that adding a new resolution (new wavelets) does not affect the existing wavelet network that may have been well tuned. In the sequel, the online adjustment of the structure of the nonlinear adaptive wavelet controller (AWC) can be done in a constructive manner by gradually increasing the network resolution. The multiresolution property, on the other hand, assures a guaranteed improvement of the approximation precision when a new resolution is added. In real life problems we are unable to know the adequate size of a network, either a neural network (NN) or a wavelet network, to produce the required approximation precision. By virtue of the novel wavelet network properties, a coarse or very simple structure can be selected first. If the system fails to converge after the elapse of a dwell time, a new wavelet resolution is considered to be necessary and added directly. In this manner, the AWC can be easily constructed and tuned from the coarse to finer levels until the performance requirement is satisfied. The trial and error way of selecting the network structure, which may lead to either an inadequate or a highly redundant structure, can be avoided. In this paper, the proposed adaptive wavelet network is applied first to a class of nonlinear dynamical systems with a partially known model and an affine-in-input structure. Then, the adaptive wavelet network is applied to a class of nonlinear nonaffine dynamical systems     

Adaptive contourlet-wavelet iterative shrinkage/thresholding for remote sensing image restoration

In this paper, we present an adaptive two-step contourlet-wavelet iterative shrinkage/thresholding （TcwlST） algorithm for remote sensing image restoration. This algorithm can be used to deal with various linear inverse problems （LIPs）, including image deconvolution and reconstruction. This algorithm is a new version of the famous two-step iterative shrinkage/thresholding （TWIST） algorithm. First, we use the split Bregrnan Rudin-Osher-Fatemi （ROF） model, based on a sparse dictionary, to decompose the image into cartoon and texture parts, which are represented by wavelet and contourlet, respectively. Second, we use an adaptive method to estimate the regularization parameter and the shrinkage threshold. Finally, we use a linear search method to find a step length and a fast method to accelerate convergence. Results show that our method can achieve a signal-to-noise ratio improvement （ISNR） for image restoration and high convergence speed.