Extraction of enhanced evoked potentials using wavelet filtering

In order to remove physiological artefacts and gain the improved evoked potentials, we propose a filtering method using the multi-resolution wavelet transform. The wavelet transform is repeatedly performed until all resolution levels are obtained. It decomposes the measured evoked potentials into scale coefficients corresponding to low frequency components and wavelet coefficients corresponding to high frequency components. In the wavelet domain, artefacts are dispersed mainly at the wavelet coefficients rather than the scaling coefficients. Thus, when the inverse wavelet transform is performed, this method shrinks the wavelet coefficients to reduce artefacts with shrinkage functions. By repeatedly performing the inverse wavelet transform, an evoked potential having the reduced artefacts and background noise is obtained. In this study, quantitative evaluation with simulation data and actual clinical data were conducted. As a result, characteristic peaks of evoked potential could be gained removing background EEG and artefacts using suggested shrinkage function. It was improved more than 0.2–1.6Db compared to the conventional averaging method. Also, the system for measuring and analyzing evoked potentials using DSP is implemented.     

小波支持向量机在SAR图像降噪中的应用

基于支持向量回归理论和小波支持向量核函数,提出了一种新的SAR滤波方法。首先对支持向量回归方法做了分析,通过对复杂信号进行逼近实验,验证了其应用于图像滤波的可行性和合理性。之后将SAR图像看成是一个二维连续信号,将对复杂信号具有更好逼近能力的小波支持向量核函数用于SAR图像滤波,小波核函数由Morlet小波构建。实验结果表明本文提出的方法能很好的降低SAR图像噪声,而且能比传统方法更好的保持边缘。     

Wavelet domain-based video noise reduction using temporal discrete cosine transform and hierarchically adapted thresholding

A novel spatio-temporal filter for video denoising, which operates entirely in the wavelet domain, is proposed. For effective noise reduction, the spatial and temporal redundancies that exist in the wavelet domain representation of a video signal are exploited. First, a 2D discrete wavelet transform is applied to the input noisy frames. This is followed by a discrete cosine transform (DCT), which is applied to the temporal subband coefficients to minimise the redundancy among the consecutive frames. The DCT transformed, noise-free coefficients in the different wavelet domain subbands for the original image sequence are modelled using a prior having a generalised Gaussian distribution. On the basis of this prior, filtering of the noisy wavelet coefficients in each subband is carried out using a new, low-complexity wavelet shrinkage method, which utilises the correlation that exists between subsequent resolution levels. Experimental results show that the proposed scheme outperforms several state-of-the-art spatio-temporal filters in terms of both the peak signal-to-noise ratio and the visual quality     

基于二进小波图像边缘的新相位水印算法

该文提出一种小波域相位水印算法。利用二进小波变换刻画图像的边缘和纹理，直接在边缘的相位成分中嵌入数字水印，水印检测利用相关检测器。实验证明，水印具有不可见性，对通常的图像处理，如JPEG压缩、剪切、缩放、中值滤波等以及噪声干扰都具有很强的鲁棒性。另外，利用私钥选择嵌入尺度层，可以保证水印的安全性。     

杂乱背景和摄像机移动下的时空兴趣点检测

提出了一种杂乱背景和摄像机移动场景下的时空兴趣点检测方法.结合空间域上的平滑和时间域上的平滑:在空间域上利用非线性各向异性扩散进行平滑,从而有效平滑掉噪声和杂乱背景中的细小结构,同时保持了人体动作的边缘信息;在时间域上利用了一个与梯度成正比的抑制函数对摄像机晃动/移动所导致的时间关系上的不确定性进行建模.本文方法有效地解决了复杂场景下时空兴趣点检测面临的杂乱背景和摄像机移动问题.     

降质图像高密度斑点批量智能识别仿真

针对降质图像中高密度斑点识别速度慢、识别位置不精准问题,提出了一种基于频域滤波法和相交测定中心法的降质图像高密度斑点批量智能识别技术。首先使用小波域内非线性软门限选取方法对图像进行去噪处理,选取小波域内非线性软门限,根据门限剔除图像冗余干扰,并确保滤除斑噪的同时保证边缘信息安全,然后使用相交测定中心算法对去噪后图像进行梯度识别,获得高密度斑点的中心面积,最后通过斑点细来设定阈值,从而完成降质图像高密度斑点批量智能识别的目的。仿真证明,相对于传统方法,所提方法在降质图像高密度斑点的批量智能识别上效率更高,并且识别精准度较高。     

一种基于小波域隐马尔可夫模型的SAR相干斑抑制算法

相干斑噪声是SAR图像的固有特点。对相干斑抑制的要求是在平滑噪声的同时，尽量保持原始图像的结构信息。现有的许多相干斑抑制方法各有优点和不足，没有普遍的适用性。基于图像在小波域的隐马尔可夫模型(HMMs)结构，结合SAR图像中相干斑噪声的统计特性，本文提出了一种新的小波域相干斑抑制方法。仿真及实测数据处理结果表明，该方法在有效抑制相干斑的同时，更好地保持了边缘结构。与小波域软阈值去噪方法和Lee滤波器相比较，该方法在噪声平滑及边缘保持上都取得了较大的改进，并得到了较好的视觉效果。     

基于非线性滤波的边缘检测算法*

线性滤波对于边缘检测的去噪处理存在着使边缘模糊、边缘检测定位精度降低的问题。为解决这个问题,分析了两种非线性滤波,即各向异性扩散滤波和双边滤波,分别将两种滤波应用在边缘检测的滤波过程。通过对比实验结果表明：非线性滤波方法可以提高边缘检测的准确率,得到更有效的边缘信息。     

基于小波变换和中值滤波的Canny边缘检测算法

传统的Canny边缘检测算法采用的是高斯平滑,用来去除图像中的计算噪声,这种去噪方法虽然对抑制高斯噪声效果较好,但对脉冲噪声等的去除并不理想。针对这一问题,提出了用小波变换与中值滤波相结合的方法取代了传统的高斯滤波法,并对平滑后的图像作图像增强。实验表明,该方法有效地提高了边缘检测的准确性,得到了比较理想的边缘检测效果。     

非线性粒子滤波目标跟踪应用仿真

针对非线性环境中存在的机动目标跟踪问题,对基于贝叶斯估计的粒子滤波器进行研究,为解决混合退火粒子滤波重要密度函数构造的问题,在混合退火粒子滤波的基础上,通过对系统状态和观测粒子方差的研究,提出了非线性环境下动态退火参数粒子滤波的改进算法,在混合退火粒子滤波中引入动态退火参数来构造高效的重要密度函数,提高了混合退火粒子滤波的跟踪精度,应用该滤波方法对机动目标模型进行仿真,并对多种滤波跟踪算法进行性能测试和比较,仿真实验结果表明,在非线性环境下该粒子滤波方法可行有效.     

Estimation of brainstem auditory evoked potentials using a nonlinear adaptive filtering algorithm

In this study, we introduce a novel nonlinear system not only for tracking of both the latency and amplitude variations in brainstem auditory evoked potential (BAEP), but also for reduction of single-trial numbers in BAEP pattern extraction process. Trial-to-trial variations in auditory evoked potential (AEP) are very important in quantifying dynamical properties of the nervous system and in specifying the group-specific effects in clinical applications. Due to the nonlinear dynamics of the AEP, a nonlinear adaptive filtering technique is considered as a powerful tool for tracking such variations. Therefore, we have designed a wavelet network-based nonlinear adaptive filter (WaNe-NAF) satisfying asymptotic stability in the sense of Lyapunov. The simulation results are verified that the proposed WaNe-NAF can effectively track the trial-to-trial variations. We have also compared the WaNe-NAF with the most widely used ensemble averaging technique using real measured human BAEP data. The WaNe-NAF shows promise for requiring less number of ensembles than conventional ensemble averaging method to attain adequate signal quality. As a result, the proposed filtering system is suggested as a powerful tool in AEP acquisition and processing systems.     

Speech enhancement with an adaptive Wiener filter

This paper proposes an adaptive Wiener filtering method for speech enhancement. This method depends on the adaptation of the filter transfer function from sample to sample based on the speech signal statistics; the local mean and the local variance. It is implemented in the time domain rather than in the frequency domain to accommodate for the time-varying nature of the speech signals. The proposed method is compared to the traditional frequency-domain Wiener filtering, spectral subtraction and wavelet denoising methods using different speech quality metrics. The simulation results reveal the superiority of the proposed Wiener filtering method in the case of Additive White Gaussian Noise (AWGN) as well as colored noise.     

Classification of evoked potentials of familiar and unfamiliar face stimuli using multi-resolution approximation based on excitatory post-synaptic potential waveform

The objective of this study is to analyze and classify evoked potentials obtained from familiar and unfamiliar face experiment. EEG signals were recorded from 26 volunteers. Multi-resolution analysis was used as a tool for signal approximation and modeling. A custom scaling-wavelet function pair and their bi-orthogonal complements were built by resembling the waveform of the scaling function to the excitatory post-synaptic potential. In order to distinguish the familiar–unfamiliar face evoked potentials, a Fisher’s linear classifier was used with discriminative approximation coefficients obtained from active electrodes which are selected by the wrapper method. The algorithm was also executed with spline, Daubechies, Symlet and Coiflet wavelets for comparison. The classification performance of proposed wavelet is the first among the other wavelets with 69.7% accuracy and it is also first in the total number of highest success of individual subjects with 31% of the subjects which is double of the result of the second wavelet in the rank.     

矿区遥感图像去噪方法研究

去噪是矿区遥感图像得以有效应用的重要预处理步骤。现有的基于统计、基于域变换、基于学习等遥感图像去噪方法普遍存在细节过度平滑、纹理保持不足等问题。基于引导滤波良好的边缘保持特性,提出了迭代引导滤波方法,通过对残差信息进行引导映射,并迭代进行引导滤波及超参数收缩,增强了遥感图像边缘特征提取效果;将迭代引导滤波与传统的小波软阈值、非局部均值(NLM)滤波、三维块匹配(BM3D)滤波等去噪方法结合,有效提高了传统方法的峰值信噪比,其中NLM滤波、BM3D滤波的去噪性能提升效果最明显;将迭代引导滤波与BM3D滤波融合,通过BM3D滤波初步获取去噪图像,得到残差数据,然后采用迭代引导滤波对残差数据进行处理,在提升图像去噪效果的同时,很好地保持了图像细节特征;将迭代引导滤波与BM3D滤波融合方法用于矿区遥感图像的煤矸石场识别及滑坡区域边缘识别,取得了较好的效果。     

基于相关分析的非线性模拟电路参数型故障诊断方法

针对非线性模拟电路故障诊断中参数型故障元件定位的难题,基于Volterra频域核的频谱比较,提出了利用小波滤波器组结合相关分析定位故障元件的方法．首先选择特定的激励信号测定Volterra频域核的非参数频谱;然后用小波滤波器组对得到的正常电路和故障电路的频谱序列进行子带分解;通过计算子带响应序列的相干函数,对正常电路和故障电路进行相关分析,实现参数型故障元件的特征提取．对比实验结果表明,该方法能有效提取故障特征,提高了故障诊断效果．     

基于小波和自适应滤波的ECG基线漂移校正

为校正ECG信号的基线漂移,提出小波变换和自适应滤波相结合的方法。利用小波变换对原始ECG信号进行分解,将得到的高频分量作为参考信号输入,采用基于幂函数的最小均方算法（P—LMS）进行自适应滤噪处理。通过与传统的归一化最小均方算法（NLMS）进行对比,验证该算法的准确性。仿真实验和MIT-BIH数据库中的实际数据检验结果表明,基于幂函数的最小均方算法和小波变换相结合的方法能够有效校正基线漂移,并较好地保持心电信号的几何特征。     

基于多小波域Besov球映射的SAR图像去噪算法

针对单小波域难以准确描述SAR图像不同平滑区域特征的不足,提出一种基于多小波域Besov球映射去噪算法。首先利用统一小波隐马尔可夫树模型和Besov标准求一组小波基Besov球半径,然后交替使用基于不同小波基的Besov球映射算法估计原始图像信息。实验结果表明,该算法具有很好的去噪效果和边缘结构保护能力,大大优于其他单一小波去噪算法。     

颤振试验中加速度计信号的时频滤波方法研究

针对飞机颤振试飞试验数据信噪比偏低的问题,提出了两种加速度计信号的时频域滤波算法,分别借助小波和分数阶傅里叶变换对数据进行时频分析,并在时频域内滤波.该类算法的共同思想是利用扫频信号在时频域内的聚焦特性,有效提取真实响应信号,达到了信噪分离的目的.文中给出了具体的滤波算法,并通过仿真算例和实际试飞数据检验了滤波效果.结果表明两种方法均可显著提高加速度计信号的信噪比.其中,小波变换方法的通用性较好,而分数阶傅里叶变换在处理线性扫频激励数据时表现了更优的去噪效果.     

Suppression of speckle in synthetic aperture radar images using wavelet

A filter for suppressing speckle in synthetic aperture radar (SAR) images utilizing wavelet is proposed. The filter suppresses speckle by reducing the amplitude of the detail images in wavelet subspaces, while preserving edges by releasing the amplitude reduction around edges; information on edges, contained in the detail images, is utilized for edge detection. Simulations and application to SAR images have shown that the performance of the filter is satisfactory in both smoothing and edge preservation, and in generating visually-natural images as well.     

小波域上图像非线性扩散滤波

提出一个小波域上图像扩散滤波恢复新模型。主要思想是把原图像作为最精细尺度下的小波子带，根据噪声分布的特点，导出保护较大尺度下信息的泛函模型代替小波阈值除噪，对泛函求变分得：Euler-Lagrange方程。新的滤波方法能避免小波阈值除噪的伪Gibbs现象，改进了同类型非线性扩散方程滤波的效果。利用可加算子分裂(AOS)格式求非线性扩散方程的数值解。实例的数值计算说明对图像滤波和保护边缘的有效性。