一种新的自适应信号处理方法——Gamma滤波

研究了一种新的自适应信号处理方法──Ｇａｍｍａ滤波．文中导出了Ｇａｍｍａ滤波的Ｗｉｅｎｅｒ－Ｈｏｐｆ方程；提出了一种对子Ｇａｍｍａ滤波的ＬＭＳ自适应算法，并对其进行了分析．最后给出了一组应用实例及仿真结果．     

一类非平稳Gauss过程的极限定理

设ｘ（ｔ）＝∫＾ｔｏｅａｘｄＷ（ｘ），它是均值为０、增量独立的非平稳Ｇａｕｓｓ过程，此处常数ａ〉０，Ｗ（ｔ）是一参数标准Ｗｉｅｎｅｒ过程，本文将建立这类过程的不可微模及有关的极限定理。     

非高斯噪声理论模型分析

分析了非高斯噪声（ＮＧＳ）理论模型研究发展过程，特别是作为ＮＧＳ理论模型的脉冲干扰造成的影响进行了分析，同时着重综述了ＮＧＳ理论模型发展现状，并给出了较详细的概率描述，对各种污染ＮＧＳ的接收信号的检测和处理给出了不同的方法，为在这种ＮＧＳ环境下信号的检测和处理提供了理论依据。     

PBCG法及其在电磁场数值计算中的应用

本文在ＢＩＣＧ法的基础上经预条件处理，建立ＰＢＣＧ法的迭代公式．然后，以电磁轨道炮数值计算得到的不对称方程组为例，比较ＰＢＣＧ法、高斯消去法．结果表明，ＰＢＣＧ法对于求解大型不对称方程组有突出的优点．     

半群同余与（）－关系

本文研究了半群相容，同余及－关系，－关系，给出了特殊半群高斯整环矩阵乘法半群Ｇｎ的（）－关系结构．     

多元Gauss—Weierstrass算子的Lp—逼近

本文借助于Ｋ－泛函和光滑模的等价关系，给出了多元Ｇａｕｓｓ－Ｗｅｉｅｒｓｔｒａｓｓ算子的Ｌｐ－逼近的特征定理。     

半群同余与L（R）—关系

本文研究了半群相容，同余及Ｌ－关系，Ｒ－关系，给出了特殊半群高斯整环矩阵乘法半群Ｇｎ的Ｌ（Ｒ）－关系结构。     

三重马氏平稳过程的一些统计性质

假定｛Ｙ（ｔ）,ｔ∈Ｒ１｝是由广义Ｗｉｅｎｅｒ积分定义的随机过程,若随机过程｛Ｙ（ｔ）｝被有界Ｂｏｒｅｌ可测函数ｆ变换,则得随机泛函ｆ（Ｙ（ｔ））,即得到新的随机过程,记为Ｑ（ｔ）,本文首先粗略地探讨三重马氏平稳过程｛Ｙ（ｔ）｝及其若干阶导数以及与它们有关的随机过程的一些概率性质。     

表冷器传热系数的实验模拟

本文利用非线性模型中参数的最小二乘估计高斯—牛顿法（Ｇ－Ｎ），提出了空气处理用表冷器传热系数实验公式的模拟方法，设计了Ｃ语言模拟程序，并根据实测数据进行了模拟结果分析。     

线性连续回归模型基于Chebyshev多项式逼近的Markov参数估计

文中先提出了基于Ｃｇｅｂｙｓｈｅｖ多项式逼近。有连续Ｗｉｅｎｅｒ过程扰动的线性连续回归模型最小二乘参数估计算法，然后讨论了Ｗｉｅｎｅｒ过程的Ｃｈｅｂｙｓｈｅｖ多项式通逼近值的相关性，在此基础上，提出了能获得参数体计误差方差为最小的Ｍａｒｋｏｖ胡数估计（最小方差估计）算法。文中还将所提出的估计方法推广至控制领域中所讨论的随机连续动态系统的参数估计中，计算机仿真结果显示了本文方法的有效性。     

Modeling Distortion Signals of Power Grid Based on Wiener-G Functionals

The uniform mathematical model of distortion signals in power grid has been setup with the theory of Wiener-G Functional. Firstly,the Matlab simulation models were established. Secondly,the Wiener kernel of power load was found based on the Gaussian white noise as input. And then the uniform mathematical model of the power grid signal was established according to the homogeneous of the same order of Wiener functional series. Finally,taking three typical distortion sources which are semiconductor rectifier,electric locomotive and electric arc furnace in power grid as examples,we have validated the model through the Matlab simulation and analyzed the simulation errors. The results show that the uniform mathematical model of distortion signals in power grid can approximation the actual model by growing the items of the series under the condition of the enough storage space and computing speed.     

基于图像块先验的低秩近似和维纳滤波的去噪算法

利用混合高斯模型(gaussian mixture model, GMM)学习自然图像块的纹理结构,提出一种基于图像块先验的低秩近似和维纳滤波的去噪算法。该算法能够同时利用外部图像块的先验结构信息和内部图像的自相似性,对待去噪图像进行分块聚类,并根据每类相似块的数量进行协同滤波。当相似图像块数量较多时,采用低秩近似的方法复原,有效利用图像的内部自相似性;当相似图像块数量较少时,采用维纳滤波,利用先验信息保持图像重要的纹理结构。试验结果表明此方法较适用于弧形边界和角点等存在较少相似块的自然图像,其峰值信噪比(peak signal to noise ratio, PSNR)和视觉效果优于目前部分主流算法。     

Multi-level denoising and enhancement method based on wavelet transform for mine monitoring

Based on low illumination and a large number of mixed noises contained in coal mine, denoising with one method usually cannot achieve good results, so a multi-level image denoising method based on wavelet correlation relevant inter-scale is presented. Firstly, we used directional median filter to effectively reduce impulse noise in the spatial domain, which is the main cause of noise in mine. Secondly, we used a Wiener filtration method to mainly reduce the Gaussian noise, and then finally used a multi-wavelet transform to minimize the remaining noise of low-light images in the transform domain. This multi-level image noise reduction method combines spatial and transform domain denoising to enhance benefits, and effectively reduce impulse noise and Gaussian noise in a coal mine, while retaining good detailed image characteristics of the underground for improving quality of images with mixing noise and effective low-light environment.     

基于小波收缩阈值和维纳滤波的去噪方法

针对高斯噪声图像的结构特点及传统去噪方法中所存在的问题,提出一种基于小波收缩阈值法和维纳滤波法相结合的图像去噪方法。采用小波收缩阈值法对图像进行去噪,对处理后的图像用维纳滤波法进行平滑处理。采用独立自适应阈值,对其子带阈值进行确定,并引入调节系数。仿真结果表明,所提出的方法在高斯去噪效果和保留图像细节信息性能方面优于中值滤波算法、均值滤波算法等方法。     

Window shrink contourlet coefficients for image denoising

An adaptive image denosing technique was proposed to achieve the tradeoff between details retain and noises removal. In order to achieve this objective, the contourlet transform was introduced and a new threshold method, namely CWinShrink, is presented. It shrinks the contourlet coefficients with adaptive shrinkage factors. The shrinkage factors were calculated with reference to the sum of squares of the contourlet coefficients within the neighborhood window. This approach achieves enhanced results for images those are corrupted with additive Gaussian noise. In numerical comparisons with various methods, for a set of noisy images （the PSNR range fi＇om 10.86dB to 26.91dB） , the presented method outperforms VisuShrink and Wiener filter in terms of the PSNR. Experiments also show that this method not only keeps the details of image but also yields denoised images with better visual quality.     

Modified Wiener method in diffusion weighted image denoising

To denoise the diffusion weighted images (DWIs) featured as multi-boundary, which was very important for the calculation of accurate DTIs (diffusion tensor magnetic resonance imaging), a modified Wiener filter was proposed. Through analyzing the widely accepted adaptive Wiener filter in image denoising fields, which suffered from annoying noise around the edges of DWIs and in turn greatly affected the denoising effect of DWIs, a local-shift method capable of overcoming the defect of the adaptive Wiener filter was proposed to help better denoising DWIs and the modified Wiener filter was constructed accordingly. To verify the denoising effect of the proposed method, the modified Wiener filter and adaptive Wiener filter were performed on the noisy DWI data, respectively, and the results of different methods were analyzed in detail and put into comparison. The experimental data show that, with the modified Wiener method, more satisfactory results such as lower non-positive tensor percentage and lower mean square errors of the fractional anisotropy map and trace map are obtained than those with the adaptive Wiener method, which in turn helps to produce more accurate DTIs.     

结合PCNN和局部维纳滤波的图像去噪

提出了一种基于脉冲耦合神经网络(PCNN)的图像去噪算法,利用PCNN的同步脉冲特性对图像小波系数进行局部加窗修正,从而对信号方差进行更好地估计,以利用局部维纳滤波进行去噪. 同时,根据各个像素间耦合特性的不同,提出了自适应连接系数,更好地反映了像素间的耦合关系,利于信号方差的估计. 实验结果表明,该算法较维纳滤波和NeighShrink算法均有较高的峰值信噪比,视觉效果更好.     

类肽图的Wiener指标

结合实际利用图论方法给出了一类骨架更复杂的类肽图——当骨架图为轮时的类肽图的Wiener，指标的计算方法及其精确结果．同时还得到了一般类肽图的Wiener指标的计算方法．本研究结果为与Wiener，指标有关的研究提供数据库。     

一种有效的小波-Wiener滤波去噪算法

把Wiener 滤波和小波变换结合起来,提出了一种小波-Wiener 滤波去噪算法. 仿真结果表明,利用该算法去噪后的图像主观质量和峰值信噪比比Wiener 滤波去噪和小波变换门限去噪后的图像都要好.     

基于块自适应窗的小波域维纳滤波图像去噪

基于图像经小波分解后细节子带能量簇的主方向由边缘方向、纹理方向和子带滤波器的方向选择性共同决定这一事实,提出了基于块自适应窗的小波域维纳滤波图像去噪算法。对含噪图像进行离散小波变换后,对每一层三个细节子带分别无重叠分块,利用细节子带块能量相关函数确定每一块中能量簇的主方向及相应的块自适应窗口,在得到的块自适应窗口中估计不同方向块中各点信号方差,进而对子带系数进行维纳滤波,得到细节系数的估计,然后进行小波逆变换,得到去噪图像。实验结果表明了该方法比基于矩形窗口的小波域局部维纳滤波有更好的去噪效果。