基于改进变分模态分解的Ф-OTDR信号去噪方法

针对相位敏感光时域反射仪(Ф-OTDR)信号信噪比过低的问题,提出了一种基于改进变分模态分解(VMD)结合独立成分分析(ICA)的去噪方法。首先,采用模拟退火方法(SA)对VMD进行优化;然后,采用SA-VMD将预处理后的Ф-OTDR信号分解成一系列本征模态分量(IMF),并根据相关准则选取IMF分量进行虚拟噪声重构;最后,将原始信号与虚拟噪声作为ICA的输入,去除信号中的噪声,提高信号信噪比。采用自行设计的相干Ф-OTDR系统进行实验验证,结果表明,该方法能够有效去除噪声,与EMD-ICA和SA-VMD方法相比,信噪比提高了4dB,这对系统的实际应用具有重要意义。     

一种改进的基于EMD分解的超宽带信号消噪算法

针对低信噪比超宽带信号的消噪问题,提出一种改进的基于经验模式分解(EMD)的消噪算法.该算法首先对含噪信号进行EMD分解,得到多个固有模态函数(IMF)分量,然后选取高阶IMF重构原信号,达到消噪的目的.针对对UWB信号的IMF重构过程中阶数阈值难以确定的问题,通过数值仿真的方法,得到信号分量和噪声分量在不同阶IMF上的能量分布特性;在对所得特性进行分析的基础上,设计了一种数据自适应的阶数阈值选取算法,解决了EMD消噪中的阶数阈值选取问题.仿真结果表明,EMD消噪算法能够在较低信噪比下提供平均10 dB的信噪比增益,可以有效地对超宽带信号进行消噪.     

基于数据驱动字典和稀疏表示的语音增强

本文提出了一种基于数据驱动字典和过完备稀疏表示的自适应语音增强方法。首先在训练阶段采用干净语音基于K奇异值分解(K singular value decomposition, K SVD)算法训练过完备字典,然后在测试阶段根据含噪语音的噪声方差自适应选择最优的阈值,采用正交匹配追踪算法对含噪语音信号在过完备字典上进行稀疏分解,最后利用系数稀疏表示重构语音信号,从而达到语音增强的目。该方法不像传统语音增强方法那样减少或消去噪声,而是从字典中选取适当的原子表示纯净信号,从而把纯净信号从含噪信号中分离出来。对白噪声和有色噪声环境下重构语音进行了主客观评价。仿真结果显示:该方法能有效去除加性噪声,并且改善了语音质量。      

EMD相关度去噪及其在轧机信号处理中的应用

基于EMD时间尺度滤波特性,在引入相关度分析的基础上提出了EMD相关度去噪方法.对含噪信号进行EMD分解得到各IMF分量,并结合相关度阈值函数计算各分量的相关度值,再与预定阈值比较获取满足阈值要求的IMF并对其进行信号重构最终得到去噪信号.该方法消除了EMD时间尺度滤波不适用于噪声和信号在IMF成分混叠情祝下的限制.通过平稳和非平稳含噪信号去噪仿真实验表明了该方法的有效性;通过轧机在轧钢时实测信号分析验证了该方法的可靠性.     

基于NSCT域压缩感知模型的路面病害图像滤波算法

针对目前路面图像滤波算法复杂度高且难以耦合噪声抑制和信号平衡的缺点,提出一种基于非下采样Contourlet变换(NSCT)域压缩采样的滤波算法。首先,使用NSCT对含噪路面病害图像进行分解,得到变换后的低频子带系数和高频子带系数;然后,对高频子带系数建立压缩感知(CS)去噪模型,并采用伪随机傅里叶矩阵对系数进行观测,之后使用分裂Bregman迭代方法对系数进行重构,得到去噪模型重建后的高频子带系数;最后,采用逆NSCT对低频子带系数和高频子带系数进行重构,得到滤波后的图像。实验结果分析表明了本文算法的有效性。     

傅里叶变换与小波变换在信号去噪中的应用

对于高频信号和高频噪声干扰相混叠的信号,采用小波变换去除噪声可以避免用傅里叶变换去噪带来的信号折损。对于噪声频率固定的平稳信号,在对信号进行傅里叶变换后使用滤波器滤除噪声。对高频含噪信号则采用正交小波函数sym4对信号分解到第4层,利用极大极小值原则选择合适的阈值进行软阈值处理,最后利用处理后的小波系数进行重构。实验结果表明,对于高频含噪信号傅里叶去噪会出现严重的信号丢失现象,使用极大极小值原则选择阈值进行小波去噪可以有效地保留高频部分的有用信号。     

基于主成分分析的经验模态分解消噪方法

 针对非线性非平稳信号的去噪问题,提出一种基于主成分分析(PCA)的经验模态分解(EMD)消噪方法.该方法根据EMD的分解特性,利用PCA对噪声信号经EMD分解后的内蕴模态函数(IMF)进行去噪处理:首先利用"3σ法则"对第一层IMF进行细节信息提取,并估计每层IMF中所含噪声的能量;然后对IMF进行PCA变换,根据IMF中所含噪声的能量选择合适数目的主成分分量进行重构,以去除IMF中的噪声.为验证本文方法的有效性,进行了数字仿真与实例应用实验.实验结果均表明,所提方法的消噪效果整体上优于Bayesian小波阈值消噪方法和基于模态单元的EMD阈值消噪方法,是一种有效的信号消噪新方法.     

基于独立分量分析的图像去噪研究

独立分量分析(independent component analysis,ICA)是基于信号高阶统计量的信号分析方法,它可以找到隐含在数据中的独立分量。在分析独立分量分析的基本模型及方法的基础上,讨论了有噪声信号的独立分量分析,使用最大似然估计对有噪声的ICA模型进行去噪处理,并研究了基于ICA的软门限图像去噪方法。在仿真实验中与其他的图像去噪方法进行了比较,突出了该方法在噪声方差较小时对非高斯信号的去噪优势。     

基于Laplace分布的非下采样小波包脉冲星信号消噪

为了提高脉冲星信号的去噪效果,提出了一种基 于非下采样小波包(NWP)分解的局部Laplace模型消噪方法。 首先对真实脉冲星信号进行NWP分解,统计真实脉冲星信号NWP系数的分布特性, 建立真实脉冲星信号小波包系数的Laplace分布模型;然后在Laplace先验概率分布的基础 上,根据最大后 验概率(MAP)估计准则,利用含噪脉冲星信号的小波包系数对真实脉冲星信号的小波包系数 进行有效估算;最后 对估算出的小波包系数进行NWP重构,得到消噪后的脉冲星信号。采用不同 的脉冲星信号进行实 验分析的结果表明,与经典的基于高斯分布的非下采样小波(NSW)消噪和NWP消噪相比,本文 方法可以 更有效地去除噪声,同时更好地保留信号中的微脉冲等细节信息,在信噪比(SNR)、均方根误差(RMSE)、相关系数(CC)和峰值相对误差(REPV)等都 有较好的改善。     

一种新的结合非下采样Contourlet与自适应全变差的图像去噪方法

该文提出了一种新的结合非下采样Contourlet变换(NSCT)和自适应全变差模型的图像去噪方法。首先通过NSCT对含噪图像进行分解,根据高斯比例混合(GSM)模型建立图像模型;然后利用贝叶斯估计进行图像去噪,重构后得到初次去噪图像;最后,结合自适应全变差模型对初次去噪图像进行重构滤波,得到最终的去噪图像。实验结果表明,该方法可以有效地消除图像中的Gibbs伪影及噪声,在去噪图像峰值信噪比(PSNR)和边缘保持性能上都优于已有的算法。     

A noise reduction technique of speech signal using ICA and spectral analysis

This paper discusses the new method on noise reduction exploiting the combined effects of wavelet decomposition, ICA and spectral analysis on noisy speech. The input noisy speech is wavelet decomposed into two signals. Wavelet entropy is computed based on the modified probability density function for the signal derived from the approximation coefficients during wavelet decomposition. By proper entropy comparison, the starting frame is detected. Between the two signals obtained from the wavelet decomposition, one is speech combined with noise and another one is noise alone. These two signals are analysed in independent component analysis (ICA) domain, in order to generate an enhanced speech. Zero-crossing rate is computed and used to discriminate between speech and noise. Then, spectral analysis is performed on the noise prior to starting frame and noisy speech. Elimination of noise frequencies in the noisy speech leads to noise reduced speech. Subjective analysis and experimental results show the considerable noise reduction capability of the proposed algorithm.     

一种改进的基于ICA的信号增强方法

根据一路带噪信号，采用独立分量分析方法去除加性噪声，从理论上讲就是在观测信号向量的维数小于源信号向量维数情况下的独立分量分析问题。问题的关键是如何构造另一路观测信号，使问题有解。文中提出了一种改进的构造方法，这种方法非常简便，实验结果表明其分离性能比较理想。     

基于独立分量分析的实正交空时分组码盲识别

提出一种基于独立分量分析的正交空时分组码(OSTBC)盲识别方法。首先给出了接收信号模型,利用独立分量分析得到含有编码矩阵的虚拟信道矩阵,然后利用编码矩阵特性,证明得到正交空时分组码的虚拟信道矩阵的相关矩阵为对角矩阵;最后提出用于正交空时分组码识别的2个特征参数:稀疏度和方差。仿真结果表明,所提出方法能够较好地识别正交空时分组码。     

A robust method based on ICA and mixture sparsity for edge detection in medical images

In this paper, a robust edge detection method based on independent component analysis (ICA) was proposed. It is known that most of the ICA basis functions extracted from images are sparse and similar to localized and oriented receptive fields. In this paper, the L ^p norm is used to estimate sparseness of the ICA basis functions, and then, the sparser basis functions were selected for representing the edge information of an image. In the proposed method, a test image is first transformed by ICA basis functions, and then, the high-frequency information can be extracted with the components of the selected sparse basis functions. Furthermore, by applying a shrinkage algorithm to filter out the components of noise in the ICA domain, we can readily obtain the sparse components of the noise-free image, resulting in a kind of robust edge detection even for a noisy image with a very low SN ratio. The efficiency of the proposed method for edge detection is demonstrated by experiments with some medical images.     

Signal propagation and noisy circuits

The information carried by a signal decays when the signal is corrupted by random noise. This occurs when a message is transmitted over a noisy channel, as well as when a noisy component performs computation. We first study this signal decay in the context of communication and obtain a tight bound on the rate at which information decreases as a signal crosses a noisy channel. We then use this information theoretic result to obtain depth lower bounds in the noisy circuit model of computation defined by von Neumann. In this model, each component fails (produces 1 instead of 0 or vice-versa) independently with a fixed probability, and yet the output of the circuit is required to be correct with high probability. Von Neumann showed how to construct circuits in this model that reliably compute a function and are no more than a constant factor deeper than noiseless circuits for the function. We provide a lower bound on the multiplicative increase in circuit depth necessary for reliable computation, and an upper bound on the maximum level of noise at which reliable computation is possible     

Optimal time-frequency deconvolution filter design fornonstationary signal transmission through a fading channel: AF filterbank approach

The purpose of this paper is to develop a new approach-time-frequency deconvolution filter-to optimally reconstruct the nonstationary (or time-varying) signals that are transmitted through a multipath fading and noisy channel. A deconvolution filter based on an ambiguity function (AF) filter bank is proposed to solve this problem via a three-stage filter bank. First, the signal is transformed via an AF analysis filter bank so that the nonstationary (or time-varying) component is removed from each subband of the signal. Then, a Wiener filter bank is developed to remove the effect of channel fading and noise to obtain the optimal estimation of the ambiguity function of the transmitted signal in the time-frequency domain. Finally, the estimated ambiguity function of the transmitted signal in each subband is sent through an AF synthesis filter bank to reconstruct the transmitted signal. In this study, the channel noise may be time-varying or nonstationary. Therefore, the optimal separation problem of multicomponent nonstationary signals is also solved by neglecting the transmission channel     

Complex-valued ICA utilizing signal-subspace demixing for robust DOA estimation and blind signal separation

This paper deals with direction of arrival (DOA) estimation and blind signal separation (BSS) based on independent component analysis (ICA) with robust capabilities. An efficient demixing procedure of complex-valued ICA is presented here, which combines the signal-subspace demixing procedure exploiting individual signal-subspace projection and Newton’s iteration algorithm based on maximization of the approximate negentropy of non-Gaussian signal for array signal processing. It resolves the problems of order ambiguity and identifiability of traditional ICA for time-domain BSS. The proposed method could be directly applied to radar, sonar, radio surveillance, and communications systems for separating signals and estimating relative DOAs of signals. Several computer simulation examples for perturbations to the array manifold, unknown noise environments, and Rayleigh fading channel are provided to illustrate the effectiveness of the proposed method.     

基于MPEG-AAC编码器的压缩域音频增强方法

本文基于MPEG-AAC音频编解码器,提出了一种压缩域的音频增强方法.首先,对含噪音频信号的比特流进行解码,得到含噪音频信号的MDCT系数;然后,利用修正的加权递归平均（Modified Weighted Recursive Averaging,MWRA）方法估计噪声功率;再者,利用基于听觉掩蔽原理的自适应β-阶双曲余弦（COSH）统计模型,对含噪音频的MDCT系数进行增强处理;最后,将增强后的MDCT系数重新量化编码,得到用于解码的增强比特流实验结果表明,本文提出的方法能有效去除AAC解码音频信号中的多种背景噪声,其性能明显优于参考方法.     

一种基于边缘信息的图像信号盲分离方法

在从多幅混合图像分离出原始图像信号的过程中,当原始图像信号之间不满足统计独立条件时,采用一般的独立分量分析方法将无法分离出正确的原始图像。针对这一缺陷,结合图像信号特点提出了一种基于图像边缘信息的独立分量分析方法。实验证明,这种方法能在一定程度上提高此类图像的分离效果,同时能有效地克服高斯白噪声的影响。