Large scale behavior of wavelet coefficients of non-linear subordinated processes with long memory

We study the asymptotic behavior of wavelet coefficients of random processes with long memory. These processes may be stationary or not and are obtained as the output of non-linear filter with Gaussian input. The wavelet coefficients that appear in the limit are random, typically non-Gaussian and belong to a Wiener chaos. They can be interpreted as wavelet coefficients of a generalized self-similar process.     

Towards artifact-free characterization of surface topography using complex wavelets and total variation minimization

A hybrid method based on dual-tree complex wavelet transform and total variation minimization is proposed for erasure of undesirable artifacts that arise in the existing wavelet-based methods in surface metrology. The complex wavelet transform provides approximate shift invariance and good directional selectivity, and attempts to solve the weakness of real discrete wavelet methods. Reconstruct the complex wavelet coefficients using a total variation minimization principle to eliminate the wavelet-shape artifacts and the pseudo-Gibbs artifacts near the discontinuities, which are caused when thresholding small wavelet coefficients. By replacing these thresholded complex wavelet coefficients by optimal values that minimize the total variation, the method performs a close artifact-free surface characterization. Numerical experiments using a series of typical engineering and bioengineering surfaces demonstrate the remarkable potential of the methodology.     

适应于图像压缩的11/9小波基构造

根据正交多分辨分析理论,利用求解低通和高通滤波的系数,可构造出多种正交小波.但正交小波中只有Haar小波满足对称性,这不适合在图像处理方面的应用.在提升格式的小波变换出现之前,小波分解通过Mallat算法来完成,而提升格式的小波有显著的优点,运算量少,不同小波运算量减少程度不一样,一般减少在25%到50%之间.文章根据双正交对称紧支集小波的消失矩、对称性、短支撑等一系列条件和其他构造原理,构造出一个适应图像压缩的11/9双正交提升小波,并满足Cohen-Daubechies准则.同时,为了便于小波变换的硬件实现,最佳的状态是,分解和重构滤波系数为二进制分数,且根据不同参数取值,让子带编码增益G_(SBC)达到最大.     

利用MATLAB符号计算求解双正交小波滤波器系数

给出了用MATLAB符号计算求解双正交小波的滤波器系数(有理数)的程序,并更正了某些文献中的滤波器系数中的错误.     

Asymptotic behavior of the threshold minimizing the average probability of error in calculation of wavelet coefficients

The problem of estimating the signal function from noisy observations by thresholding the coefficients of its wavelet decomposition is considered. The asymptotic orders of the threshold and risk are calculated by minimizing the average probability of error in calculating the wavelet coefficients.     

The wavelet detection of the jump and cusp points of a regression function

1. IntroductionMuch effort has been taken to detect the change points of a noise contaminated signal. Detection of change points is very useful in dealing with practical problems such assignal analysis, image processing and phonetic identification. For example, in dealing withelect ro encep halogr am signal ? do ct ors of t en need t o find re al sharp cusp s which exhibi t t heaccelerations and decelerations in the beating of hearts. The early work on detection ofthe change points of a regres…     

Characterization of Function Spaces Using Wavelets

It is well known that we can use wavelets to characterize various function spaces, for example, Lebesgue, Sobolev, and Besov spaces, and get equivalent norms with wavelet coefficients. However, we cannot determine whether a function is in these spaces by looking only at the wavelet coefficients since the constant function is orthogonal to all wavelets. In this paper, we close the gap by investigating the convergence of wavelet series.     

期权定价的小波方法

基于Daubechies正交小波,对微分算子进行小波近似,从而求解Black-Scholes方程,为期权定价提出了一种新的尝试.通过偏微分算子和小波系数的稀疏化,相对二叉树法,大大减少了计算量,提高了运算速度.     

Wavelet method for a class of fractional convection-diffusion equation with variable coefficients

A wavelet method to the solution for a class of space–time fractional convection-diffusion equation with variable coefficients is proposed, by which combining Haar wavelet and operational matrix together and dispersing the coefficients efficaciously. The original problem is translated into Sylvester equation and computation became convenient. The numerical example shows that the method is effective.     

基于四点插值细分小波的SAR图像去噪

针对SAR图像去噪过程中存在降低相干斑与保持有效细节这一矛盾,提出了一种基于四点插值细分小波的SAR图像去噪算法,该方法将小波和细分方法相融合,将四点插值细分规则应用到细分小波中,提出了图像去噪的新方法.该算法先用四点插值细分小波对原始图像进行分解,然后用Bayes自适应阈值及阈值函数对图像进行去噪,最后对去噪的小波系数进行重构,并通过等效视数、边缘保持指数等评价指标对去噪结果进行了评价.实验结果表明,算法的等效视数、边缘保持指数都有所提高,去噪效果得到了优化.     

Wavelet Approximation of Periodic Functions

We investigate expansions of periodic functions with respect to wavelet bases. Direct and inverse theorems for wavelet approximation in C and L_p norms are proved. For the functions possessing local regularity we study the rate of pointwise convergence of wavelet Fourier series. We also define and investigate the “discreet wavelet Fourier transform” (DWFT) for periodic wavelets generated by a compactly supported scaling function. The DWFT has one important advantage for numerical problems compared with the corresponding wavelet Fourier coefficients: while fast computational algorithms for wavelet Fourier coefficients are recursive, DWFTs can be computed by explicit formulas without any recursion and the computation is fast enough.     

Smoothness Estimates for Soft-Threshold Denoising via Translation-Invariant Wavelet Transforms

In this paper, we study a generalization of the Donoho–Johnstone denoising model for the case of the translation-invariant wavelet transform. Instead of soft-thresholding coefficients of the classical orthogonal discrete wavelet transform, we study soft-thresholding of the coefficients of the translation-invariant discrete wavelet transform. This latter transform is not an orthogonal transformation. As a first step, we construct a level-dependent threshold to remove all the noise in the wavelet domain. Subsequently, we use the theory of interpolating wavelet transforms to characterize the smoothness of an estimated denoised function. Based on the fact that the inverse of the translation-invariant discrete transform includes averaging over all shifts, we use smoother autocorrelation functions in the representation of the estimated denoised function in place of Daubechies scaling functions.     

基于自适应小波变换和矢量量化的图像压缩编码

运用小波变换进行图像压缩的算法其核心都是小波变换的多分辨率分析以及对不同尺度的小波系数的量化和编码 .本文提出了一种基于能量的自适应小波变换和矢量量化相结合的压缩算法 .即在一定的能量准则下 ,根据子图像的能量大小决定是否进行小波分解 ,然后给出恰当的小波系数量化 .在量化过程中 ,采用一种改进的LBG算法进行码书的训练 .实验表明 ,本算法广泛适用于不同特征的数字图像 ,在取得较高峰值信噪比的同时可以获得较高的重建图像质量 .     

Beyond Besov Spaces,Part 2: Oscillation Spaces

We study several extensions of Besov spaces; these extensions include the oscillation spaces O_p^s,swhich take into account correlations between the positions of large wavelet coefficients through the scales and, more generally, spaces defined through the distributions of suprema of wavelet coefficients on wavelet subtrees. These spaces are independent of the particular wavelet basis chosen. Examples of applications will be taken from image processing and multifractal analysis.     

Bayesian Inference with Wavelets: Density Estimation

Abstract

We propose a prior probability model in the wavelet coefficient space. The proposed model implements wavelet coefficient thresholding by full posterior inference in a coherent probability model. We introduce a prior probability model with mixture priors for the wavelet coefficients. The prior includes a positive prior probability mass at zero which leads to a posteriori thresholding and generally to a posteriori shrinkage on the coefficients. We discuss an efficient posterior simulation scheme to implement inference in the proposed model. The discussion is focused on the density estimation problem. However, the introduced prior probability model on the wavelet coefficient space and the Markov chain Monte Carlo scheme are general.     

Detection of the jump points of a heteroscedastic regression model by wavelets

1. IntroductionDetection of jump points often arises in many practical problems such as signal analysis,.... fimage processing, seismic exploratioll and phonetic identification. FOr examPle, financialeconollilsts often wad to know if abrupt changes occur in an exchange rate series sincethese changes edicted, are affecting or will affect fin-ancial market; engineers concern abolltwhether there exist jumps in a seismic signal in oil exploration bacause these jumps maypredict that there exists br…     

Hardy空间H~1(R)的紧小波框架系数刻画

利用标准正交小波基下函数的展开系数来刻画Hardy空间H~1(R)已经得到了很好的证明.该文利用紧小波框架与标准正交小波基的关系及其性质,给出了Hardy空间H~1(R)在紧小波框架下函数展开系数的一个刻画.     

具有一定消失矩的优化线性相位滤波器

利用消失矩特性和编码误差最小,给出了一类有限长线性相位的双正交小波滤波器组BNVF的构造方法,BNVF的综合低通和分解高通的系数为二进分数,将其用于图像的分解与重构时,可避免一些乘法运算．三组BNVF用于图像变换编码时,其压缩性能不亚于Cohen,Daubechies等构造的9／7步滤波器组CDF-9／7,且计算复杂度更低．     

Estimation of Time Varying Linear Systems

Based on kernel and wavelet estimators of the evolutionary spectrum and cross-spectrum we propose nonlinear wavelet estimators of the time varying coefficients of a linear system, whose input and output are locally stationary processes, in the sense of Dahlhaus (1997). We obtain large sample properties of these estimators, present some simulated examples and derive results on the L ₂-risk for the wavelet threshold estimators, assuming that the coefficients belong to some smoothness class. This revised version was published online in June 2006 with corrections to the Cover Date.     

A remark on the mallat pyramidal algorithm of wavelet analysis wavelet analysis

The exact relationships between the length of scale sequences and wavelet sequences in the Mallat pyramidal algorithm for computing wavelet transform coefficients are obtained, and the maximum possible scale of arbitrary discrete signal is derived.