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.     

Optimal FPGA implementation of CL multiwavelets architecture for signal denoising application

Wavelet transform is considered one of the efficient transforms of this decade for real time signal processing. Due to implementation constraints scalar wavelets do not possess the properties such as compact support, regularity, orthogonality and symmetry, which are desirable qualities to provide a good signal to noise ratio (SNR) in case of signal denoising. This leads to the evolution of the new dimension of wavelet called ‘multiwavelets’, which possess more than one scaling and wavelet filters. The architecture implementation of multiwavelets is an emerging area of research. In real time, the signals are in scalar form, which demands the processing architecture to be scalar. But the conventional Donovan Geronimo Hardin Massopust (DGHM) and Chui-Lian (CL) multiwavelets are vectored and are also unbalanced. In this article, the vectored multiwavelet transforms are converted into a scalar form and its architecture is implemented in FPGA (Field Programmable Gate Array) for signal denoising application. The architecture is compared with DGHM multiwavelets architecture in terms of several objective and performance measures. The CL multiwavelets architecture is further optimised for best performance by using DSP48Es. The results show that CL multiwavelet architecture is suited better for the signal denoising application.     

The application of multiwavelet filterbanks to image processing

Multiwavelets are a new addition to the body of wavelet theory. Realizable as matrix-valued filterbanks leading to wavelet bases, multiwavelets offer simultaneous orthogonality, symmetry, and short support, which is not possible with scalar two-channel wavelet systems. After reviewing this theory, we examine the use of multiwavelets in a filterbank setting for discrete-time signal and image processing. Multiwavelets differ from scalar wavelet systems in requiring two or more input streams to the multiwavelet filterbank. We describe two methods (repeated row and approximation/deapproximation) for obtaining such a vector input stream from a one-dimensional (1-D) signal. Algorithms for symmetric extension of signals at boundaries are then developed, and naturally integrated with approximation-based preprocessing. We describe an additional algorithm for multiwavelet processing of two-dimensional (2-D) signals, two rows at a time, and develop a new family of multiwavelets (the constrained pairs) that is well-suited to this approach. This suite of novel techniques is then applied to two basic signal processing problems, denoising via wavelet-shrinkage, and data compression. After developing the approach via model problems in one dimension, we apply multiwavelet processing to images, frequently obtaining performance superior to the comparable scalar wavelet transform.     

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.     

基于图像内容的多子波去噪

本文介绍了多子波变换的一般理论及OPTFR多子波的性质,通过改进其一阶平衡器,避免了预滤波,从而简化了运算。在将OPTFR多子波用于图像处理时,采用了一种基于图像内容的阈值方法,实验结果表明,其去噪效果优于CL多子波及一般的阈值方法。     

Data-driven and optimal denoising of a signal and recovery of its derivative using multiwavelets

Multiwavelets are relative newcomers into the world of wavelets. Thus, it has not been a surprise that the used methods of denoising are modified universal thresholding procedures developed for uniwavelets. On the other hand, the specific of a multiwavelet discrete transform is that typical errors are not identically distributed and correlated, whereas the theory of the universal thresholding is based on the assumption of identically distributed and independent normal errors. Thus, we suggest an alternative denoising procedure based on the Efromovich-Pinsker algorithm. We show that this procedure is optimal over a wide class of noise distributions. Moreover, together with a new cristina class of biorthogonal multiwavelets, which is introduced in this paper, the procedure implies an optimal method for recovering the derivative of a noisy signal. A Monte Carlo study supports these conclusions.     

多小波的预处理及其在图像压缩中的应用

多小波是近几年小波理论研究的一个重要方向,但在图像压缩中还没有成功的例子;本文综述了多小波的重要性质,对几个常用多小波作了预处理;图像压缩应用实验结果表明平衡处理比预滤波效果好,尤其是Opt-recl多小波能精确重构,无边界失真,在一定的压缩比下可达到很高的峰值信噪比PSNR.     

基于遗传算法的多小波自适应去噪方法研究

针对噪声在多小波分解后的尺度性以及图像本身的特性,提出了一种基于遗传算法的多小波自适应去噪算法,该方法能通过遗传算法自适应地寻求去噪后图像的最小均方误差.实验结果表明,该算法优于传统算法,不仅能有效滤除图像的噪声,而且能较好地保留图像的边缘信息,具有更加理想的去噪效果.     

Translation-invariant denoising using multiwavelets

Translation invariant (TI) single wavelet denoising was developed by Coifman and Donoho (1994), and they show that TI is better than non-TI single wavelet denoising. On the other other hand, Strela et al. (1994) have found that non-TI multiwavelet denoising gives better results than non-TI single wavelets. We extend Coifman and Donoho's TI single wavelet denoising scheme to multiwavelets. Experimental results show that TI multiwavelet denoising is better than the single case for soft thresholding     

基于单小波和多小波的红外图像盲去噪

Introduction Infrared Image denoising is a basic problem of image processing.It is well known that the more prioriknowledge is used,the better filtering effect is gotten.Usually,we can only get a contaminated image and prioriknowledge of noise can not be gotten accurately so the filtering effect is not good.Wavelet denoising algorithmproposed by Donoho et al.[1]is widely used.It is considered the better denoising algorithm recently.However,itrequires size of image to determine the important …     

对含脉冲噪声的图像去噪算法的研究

在传统均值滤波和中值滤波的基础上,结合聚类算法理论,采用硬聚类算法和模糊模型两种算法消除图像中的脉冲噪声。与传统的滤波算法和硬聚类模型去噪算法相比,基于模糊模型的去噪算法更好地提高了图像的清晰度和信噪比。     

New denoising scheme for magnetic resonance spectroscopy signals

A new scheme for denoising magnetic resonance spectroscopy (MRS) signals is presented. This scheme is based on projecting noisy MRS signals in different domains, consecutively, and performing noise filtering operations in these domains. The domains are chosen such that the noise portion, which is inseparable from the desired signal in one domain, is separable in the other. A set of stable, linear, time-frequency (SLTF) transforms with different resolutions was selected for these projections as an example. Scheme evaluation was performed using extensive MRS signals with various noise levels. Compared with one domain denoising, it was observed that the proposed scheme gives superior results that compensate for the excess computational requirements. The proposed scheme supersedes also the wavelet packet denoising schemes.     

Optimizing the multiwavelet shrinkage denoising

Denoising methods based on wavelet domain thresholding or shrinkage have been found to be effective. Recent studies reveal that multivariate shrinkage on multiwavelet transform coefficients further improves the traditional wavelet methods. It is because multiwavelet transform, with appropriate initialization, provides better representation of signals so that their difference from noise can be clearly identified. We consider the multiwavelet denoising by using multivariate shrinkage function. We first suggest a simple second-order orthogonal prefilter design method for applying multiwavelet of higher multiplicities. We then study the corresponding thresholds selection using Stein's unbiased risk estimator (SURE) for each resolution level provided that we know the noise structure. Simulation results show that higher multiplicity wavelets usually give better denoising results and the proposed threshold estimator suggests good indication for optimal thresholds.     

一种选择多小波预滤波器的方法

提出了多小波(Multiwavelet)预滤波器的选择原则—预滤波和多小波变换对原数据不引入任何形式的信息损失或失真,该原则对应于预滤波器和多小波滤波器组在频域内能谱密度恒为常数．在此基础上,得到了常用多小波预滤波器应满足的必要条件．提出了用于数据压缩的预滤波器的选择方法,该方法以数据高低频成分分离的分离效率为选择准则．以此准则,给出了常用多小波的最佳预滤波器．仿真实例证实了这种方法的有效性．     

1/f信号的积分多小波变换表示

在积分多小波变换理论的基础上,研究了1/f信号的积分多小波变换表示,给出了用积分多小波逆变换产生1/f信号(非近似1/f信号)的条件。并对1/f信号的积分多小波变换的统计特性进行了研究,证明1/f信号的统计自相似性在多小波域内可由积分多小波系数的自相关函数矩阵来表达。基于单小波变换的1/f信号表示是其特例。     

基于M带小波变换与模糊聚类的图像去噪算法

M带小波变换是标准二带小波变换的自然推广,能够分析具有相对窄带的高频信号,而且能更好的集中信号能量,因此在信号处理中应用广泛。本文结合模糊聚类算法,提出了一种新的基于M带小波变换的图像去噪算法,利用模糊聚类算法把小波系数划分成两类：包含信号的小波系数与只包含噪声的小波系数,对只包含噪声的小波系数置为零,将包含信号的小波系数进行利用软阈值法进行收缩,最后对处理后的系数进行M带小波逆变换,得到去噪后的图像。对SAR图像的实验结果表明,该算法有效,而且能较好地保留边缘信息。     

基于平稳多小波变换的红外图像噪声抑制方法

提出了一种平稳多小波变换方法,该方法结合多小波和平稳小波变换在信号去噪方面的优点,给出了二维图像平稳多小波变换的mallat分解重构算法,并对红外图像的平稳多小波变换系数进行阚值处理实现图像去噪,仿真结果表明,相对于平稳标量小波变换和多小波的噪声抑制方法,此方法对噪声有更好的抑制作用,并尽可能多的保持目标的特征和细节．     

Simultaneous Speckle Reduction and SAR Image Compression Using Multiwavelet Transform

Synthetic aperture radar （SAR） images are corrupted by multiplicative speckle noise which limits the performance of the classical coder/decoder algorithm in spatial domain. The relatively new transform of multiwavelets can possess desirable features simultaneously, such as orthogonality and symmetry, while scalar wavelets cannot. In this paper we propose a compression scheme combining with speckle noise reduction within the multiwavelet framework. Compared with classical set partitioning in hierarchical trees （SPIHT） algorithm, our method achieves favorable peak signal to noise ratio （PSNR） and superior speckle noise reduction performances.     

基于小波阈值去噪和共轭模糊函数的时频差联合估计算法

为了解决二阶互模糊函数对相关噪声处理的局限性问题,以及基于四阶累积量的联合估计算法的运算量大的问题,该文利用小波阈值去噪方法结合非圆信号的特性,提出一种新的时频差联合估计算法。该方法首先对接收信号进行小波阈值去噪,然后构造共轭模糊函数,最后再进行2维搜索,得到时差和频差参数。仿真实验给出不同信噪比下的参数估计结果,得出这种算法能抑制相关噪声,又能相对降低运算复杂度,并且在较低信噪比下也能做出准确估计。     

多小波:理论和应用

多小波是单小波的推广．普通的单小波不可能同时具有对称、正交、有限支撑等性质;而多小波放松了对单小波函数的限制,可以同时拥有这些特性,将会有更广阔的应用前景．本文首先介绍多小波的基本理论,然后举例说明在使用多小波中会碰到的预处理问题及其解决的方法．