针对拖尾噪声的中值滤波-小波消噪算法分析

与高斯噪声相比,拖尾噪声有更多的异常值,利用传统的小波阈值方法不能对其有效消噪.提出利用中值滤波-小波消噪方法进行处理,首先利用中值滤波抑制异常值,然后利用小波阈值方法消除残留噪声,并给出了适合拖尾噪声的消噪效果评价准则.基于提出的准则,通过实验比较了小波阈值方法与中值滤波-小波消噪方法的消噪效果,结果表明所提出的方法能更好的消除拖尾噪声,具有较好的鲁棒性.     

基于图像中值灰度相似度函数加权曲率滤波算法

曲率滤波算法通过构造滤波算子快速优化变分模型,但全变分曲率滤波及高斯曲率滤波易致去噪过平滑且椒盐噪声去除较差.提出了基于图像中值灰度相似度函数加权曲率滤波算法,其中,中值灰度相似度函数方差取决于小波变换最高频子带系数,能较好防止图像过平滑,且提高椒盐噪声去除能力;因此,采用中值灰度相似度函数分别对局部高斯曲率与局部全变分曲率投影算子加权,并分别迭代局部加权高斯曲率投影算子与局部加权全变分曲率投影算子,直至输出图像梯度总能量满足停止条件.实验表明,基于图像中值灰度相似度函数加权全变分曲率滤波与加权高斯曲率滤波比传统全变分曲率滤波和高斯曲率滤波去噪效果更好.     

保持图像细节的局部自适应去噪滤波器新方法

去噪算法在现代图像处理应用中占有极其重要的地位。然而图像去噪的过程总是伴随着图像的模糊。本文提出了一种对彩色图像细节保持较好的局部自适应的去噪算法--基于个数判断噪声的前提下结合线性插值、非线性插值(这里主要指中值滤波)滤波对噪声图像进行处理,得到较理想的效果。和领域平均法、倒数梯度加权法、纯线性插值法、纯中值滤波法等相比较,其效果改善明显。     

基于准十字窗口的中值滤波法在红外图像处理中的应用

中值滤波法是图像处理中一种非线性滤波技术,可对图像中的噪声进行有效的抑制,成为空域平滑的重要手段。针对中值滤波法的不足,提出一种基于准十字窗口的中值滤波方法。给出了抑制噪声、对图像边界最大保留的计算方法和运算步骤。并将其用于某型飞机实际红外图像处理实践,利用空间噪声曲线对该方法进行了验证。实践证明了其方法具有简单有效、易于编程实现、迭代性好、计算速度快的特点。     

基于先验知识的椒盐噪声动态去噪算法

椒盐噪声产生于图像的传输过程,对后续图像处理有较大的干扰。虽然中值滤波类的方法被证明对椒盐噪声有不错的处理效果,但图像中边缘信息模糊严重。为此,提出了一种基于局部像素点的值关系的椒盐噪声去噪算法,该算法先用椒盐噪声的特点对噪声点定位,然后用局部像素值之间的线性关系对图像窗口滤波。用峰值信噪比与基于开关的自适应中值滤波的对比实验结果证明,使用本文的方法不但信噪比优于对比算法,而且更好地保护了图像的边缘信息。     

中值滤波在激光雷达点云数据预处理中的应用

为了研究3维激光雷达测量系统采集到的点云数据如何进行去噪处理,根据灰度图像中对灰度值进行滤波去噪的概念,采用改进的2维中值滤波方法对点云数据中的噪声点加以处理。基于激光雷达点云数据数据量庞大且存在噪声点特点,重点分析了改进2维中值滤波算法和点云数据信噪分离方法,并通过实验验证,得到了速度对比数据和滤波效果图。结果表明,利用改进后的2维中值滤波方法,速度明显得到改进,对激光雷达点云数据的滤波效果良好。     

基于Matlab的椒盐噪声滤波算法研究与实现

介绍了图像去噪流程,研究了图像椒盐噪声处理中的两种算法,均值滤波算法和中值滤波算法,详细阐述了两种算法的基本原理和实现方法,在Matlab环境下利用两种算法对图像进行去噪处理,并对去噪结果进行比较、分析,实验结果表明两种算法都能有效滤除图像中的椒盐噪声,中值滤波算法在保护图像细节方面要优于均值滤波算法。     

一种基于极值的自适应中值滤波算法

图像平滑处理中,如何在去除噪声的同时完整地保留图像边缘细节一直是非线性滤波算法研究的热点问题。提出了一种基于极值的自适应中值滤波算法,该算法根据图像中某点是否为邻域极值点将全部像素分为可疑噪声与信号两类。对可疑噪声点采用包括八个一维窗口和一个二维窗口在内的不同尺度和不同方向的九个子窗口,按照各个子窗口的均方差大小,自适应选择窗口进行中值滤波;对信号点不加处理,灰度值不变。测试结果表明,该算法的滤噪特性和细节保护能力优于多级中值滤波;执行速度较快,优于经典中值滤波。     

数字图像中值滤波技术研究

中值滤波作为图像处理中的一种非线性滤波技术,在有效抑制脉冲噪声的同时能很好地保护图像信号的细节信息,尤其是在处理椒盐噪声方面效果较好,得到了广泛的研究和应用。文章通过对中值滤波及其改进方法的研究,比较了不同方法的运算效率及对不同图像的去噪效果,分析中值滤波技术的研究方向。     

基于小波变换的图像去噪方法

针对图像中存在的高斯噪声、椒盐噪声和二者混合噪声,提出了一种基于小波变换的图像去噪方法。为进一步提高图像去噪质量,采用Bayes Shrink和中值滤波相结合的方法,对其的不同去噪顺序进行实验,并与中值滤波、Bayes Shrink方法相比较。实验结果表明,先进行Bayes Shrink再进行中值滤波的方法要优于其他方法,去噪效果较好。在图像去噪处理中该种方法具有实际应用价值。     

Recursive blind identification of non-Gaussian time-varying ARmodel and application to blind equalisation of time-varying channel

A novel method for the blind identification of a non-Gaussian time-varying autoregressive model is presented. By approximating the non-Gaussian probability density function of the model driving noise sequence with a Gaussian-mixture density, a pseudo maximum-likelihood estimation algorithm is proposed for model parameter estimation. The real model identification is then converted to a recursive least squares estimation of the model time-varying parameters and an inference of the Gaussian-mixture parameters, so that the entire identification algorithm can be recursively performed. As an important application, the proposed algorithm is applied to the problem of blind equalisation of a time-varying AR communication channel online. Simulation results show that the new blind equalisation algorithm can achieve accurate channel estimation and input symbol recovery     

基于图像背景噪声特性的篡改检测

数字图像都包含有一部分来自成像过程或者数字压缩的背景噪声,如果两幅不同背景噪声的图像被拼接在一起,则图像篡改区域和其他区域的噪声特性会有差异。本文基于一种估计信道信噪比的高阶统计量法提出了一种新的图像背景噪声的盲估计算法。通过对图像进行分块计算每块的噪声方差,从而检测图像篡改部分。此算法通过二次加噪的方法解决了高阶统计量法中必须已知原始信号的问题,实现了待检测图像噪声的盲估计。实验结果显示该算法能有效估计图像的噪声方差从而达到检测局部篡改的目的。并且图像的缩放和压缩对检测结果影响很小,算法具有较好的鲁棒性。      

优化重加权L1范数的图像盲复原算法

在单幅运动模糊图像的盲复原问题中,图像中强边缘部分的利用成为模糊核估计的关键所在.为此,本文提出了一种优化重加权L1范数的图像盲复原算法.首先,建立了基于加权L1范数的模糊核盲估计模型,并引入了一种图像平滑模型对权重进行优化估计,从而减少计算权重时受细小结构以及噪声的影响,其次,设计了模糊核盲估计模型求解的迭代收缩阈值数值算法,最后采用了一种基于超拉普拉斯先验的快速图像非盲复原算法对模糊图像进行复原.仿真和实际数据实验结果验证了本文算法的有效性.     

两次一维维纳滤波信道估计的 一种噪声方差优化方法

 本文提出了一种基于OFDM(Orthogonal Frequency Division Multiplexing)系统的两次一维(2×1-D)维纳滤波信道估计的噪声方差优化方法.对于2×1-D维纳滤波信道估计,维纳滤波将先后应用于频域维和时域维,而两次滤波时的噪声方差实际是不相同的,但现有的2×1-D维纳滤波信道估计方法没有考虑噪声的变化.本文首先分析出了第一次滤波后残余的噪声方差,并将其优化的结果应用于第二次滤波中,然后根据不同的优化准则对信道估计性能进行了评估.仿真结果表明,同未对噪声方差优化的信道估计方法相比,本方法具有更优的性能,且非常接近两维维纳(2-D)滤波方法.     

Array processing in correlated noise fields based on instrumentalvariables and subspace fitting

Accurate signal parameter estimation from sensor array data is a problem which has received much attention in the last decade. A number of parametric estimation techniques have been proposed in the literature. In general, these methods require knowledge of the sensor-to-sensor correlation of the noise, which constitutes a significant drawback. This difficulty can be overcome only by introducing alternative assumptions that enable separating the signals from the noise. In some applications, the raw sensor outputs can be preprocessed so that the emitter signals are temporally correlated with correlation length longer than that of the noise. An instrumental variable (IV) approach can then be used for estimating the signal parameters without knowledge of the spatial color of the noise. A computationally simple IV approach has recently been proposed by the authors. Herein, a refined technique that can give significantly better performance is derived. A statistical analysis of the parameter estimates is performed, enabling optimal selection of certain user-specified quantities. A lower bound on the attainable error variance is also presented. The proposed optimal IV method is shown to attain the bound if the signals have a quasideterministic character     

多传感器分布式融合白噪声反卷积滤波器

基于Kalman滤波方法和白噪声估计理论,在按矩阵加权线性最小方差最优融合准则下,提出了带ARMA有色观测噪声系统的多传感器分布式融合白噪声反卷积滤波器,其中推导出用Lyapunov方程计算最优加权的局部估计误差互协方差公式。与单传感器情形相比,可提高融合估值器精度。它可应用于石油地震勘探信号处理。一个三传感器分布式融合Bernoulli-Gauss白噪声反卷积平滑器的仿真例子说明了其有效性。     

不确定系统鲁棒协方差交叉融合稳态Kalman滤波器

针对带不确定模型参数和噪声方差的线性离散多传感器系统,基于极大极小鲁棒估值原理,该文提出一种鲁棒协方差交叉(CI)融合稳态Kalman滤波器。首先,用引入虚拟噪声补偿不确定模型参数,把模型参数和噪声方差两者不确定的多传感器系统转化为仅噪声方差不确定的系统。其次,应用Lyapunov方程证明局部鲁棒Kalman滤波器的鲁棒性,进而保证CI融合Kalman滤波的鲁棒性,且证明了CI融合器的鲁棒精度高于每个局部滤波器的鲁棒精度。最后,给出一个仿真例子来说明如何搜索不确定参数的鲁棒域,并验证所提出的鲁棒Kalman滤波器的优良性能。     

Information fusion steady-state white noise deconvolution estimators with time-delayed measurements and colored measurement noises

White noise deconvolution or input white noise estimation problem has important application backgrounds in oil seismic exploration, communication and signal processing. By the modern time series analysis method, based on the Auto-Regressive Moving Average (ARMA) innovation model, under the linear minimum variance optimal fusion rules, three optimal weighted fusion white noise deconvolution estimators are presented for the multisensor systems with time-delayed measurements and colored measurement noises. They can handle the input white noise fused filtering, prediction and smoothing problems. The accuracy of the fusers is higher than that of each local white noise estimator. In order to compute the optimal weights, the formula of computing the local estimation error cross-covariances is given. A Monte Carlo simulation example for the system with 3 sensors and the Bernoulli-Gaussian input white noise shows their effectiveness and performances.     

Speech Enhancement Combining Optimal Smoothing and Errors-In-Variables Identification of Noisy AR Processes

In the framework of speech enhancement, several parametric approaches based on an a priori model for a speech signal have been proposed. When using an autoregressive (AR) model, three issues must be addressed. (1) How to deal with AR parameter estimation? Indeed, due to additive noise, the standard least squares criterion leads to biased estimates of AR parameters. (2) Can an estimation of the variance of the additive noise for each speech frame be obtained? A voice activity detector is often used for its estimation. (3) Which estimation rules and techniques (filtering, smoothing, etc.) can be considered to retrieve the speech signal? Our contribution in this paper is threefold. First, we propose to view the identification of the noisy AR process as an errors-in-variables problem. This blind method has the advantage of providing accurate estimations of both the AR parameters and the variance of the additive noise. Second, we propose an alternative algorithm to standard Kalman smoothing, based on a constrained minimum variance estimation procedure with a lower computational cost. Third, the combination of these two steps is investigated. It provides better results than some existing speech enhancement approaches in terms of signal-to-noise-ratio (SNR), segmental SNR, and informal subjective tests.     

Turbo estimation algorithms: general principles, and applications to modal analysis

In this paper, a new class of parameter estimation algorithms, called turbo estimation algorithms (TEA), is introduced. The basic idea is that each estimation algorithm (EA) must perform a sort of intrinsic denoising of the input data in order to achieve reliable estimates. Optimum algorithms implement the best possible noise reduction, compatible with the problem definition and the related lower bounds to the estimation error variance; however, their computational complexity is often overwhelming, so that in real life one must often resort to suboptimal algorithms; in this case, some amount of noise could be still eliminated. The TEA methods reduce the residual noise by means of a closed loop configuration, in which an external denoising system, fed by the master estimator output, generates an enhanced signal to be input to the estimator for next iteration. The working principle of such schemes can be described in terms of a more general turbo principle, well known in an information theory context. In this paper, an example of turbo algorithm for modal analysis is described, which employs the Tufts and Kumaresan (TK) method as a master EA.