Combined Adaptive Multiple Subtraction Based on Optimized Event Tracing and Extended Wiener Filtering

The surface-related multiple elimination(SRME) method is based on feedback formulation and has become one of the most preferred multiple suppression methods used. However, some differences are apparent between the predicted multiples and those in the source seismic records, which may result in conventional adaptive multiple subtraction methods being barely able to effectively suppress multiples in actual production. This paper introduces a combined adaptive multiple attenuation method based on the optimized event tracing technique and extended Wiener filtering. The method firstly uses multiple records predicted by SRME to generate a multiple velocity spectrum, then separates the original record to an approximate primary record and an approximate multiple record by applying the optimized event tracing method and short-time window FK filtering method. After applying the extended Wiener filtering method, residual multiples in the approximate primary record can then be eliminated and the damaged primary can be restored from the approximate multiple record. This method combines the advantages of multiple elimination based on the optimized event tracing method and the extended Wiener filtering technique. It is an ideal method for suppressing typical hyperbolic and other types of multiples, with the advantage of minimizing damage of the primary. Synthetic and field data tests show that this method produces better multiple elimination results than the traditional multi-channel Wiener filter method and is more suitable for multiple elimination in complicated geological areas.

Noise degradation system using Wiener filter and CORDIC based FFT/IFFT processor

On augmentation of past work, an effective Wiener filter and its application for noise suppression combined with a formed CORDIC based FFT/IFFT processor with improved speed were executed. The pipelined methodology was embraced for expanding the execution of the system. The proposed Wiener filter was planned in such an approach to evacuate the iteration issues in ordinary Wiener filter. The division process was supplanted by a productive inverse and multiplication process in the proposed design. An enhanced design for matrix inverse with reduced computation complexity was executed. The wide-ranging framework processing was focused around IEEE-754 standard single precision floating point numbers. The Wiener filter and the entire system design was integrated and actualized on VIRTEX 5 FPGA stage and re-enacted to approve the results in Xilinx ISE 13.4. The results show that a productive decrease in power and area is developed by adjusting the proposed technique for speech signal noise degradation with latency of n/2 clock cycles and substantial throughput result per every 12 clock cycles for n-bit precision. The execution of proposed design is exposed to be 31.35% more effective than that of prevailing strategies.

Receiver Function Estimated by Wiener Filtering

Wiener filtering is used to estimate receiver function in a time_domain. With the vertical component of 3_component teleseismic P waveform as the input of a Wiener filter, receiver function as the filter response, and radial and tangential components as the expected output, receiver function is estimated by minimizing the error between expected and actual outputs. Receiver function can be obtained by solving the Toeplitz equation using the Levinson algorithm. The non_singularity of the Toeplitz equation ensures the stability of Wiener Deconvolution. Both synthetic and observational seismogram checks show that Wiener Deconvolution is an effective time_domain method to estimate receiver function from teleseismic P waveform.

Speech Enhancement Using Cross-Correlation Compensated Multi-Band Wiener Filter Combined with Harmonic Regeneration

The speech signal in general is corrupted by noise and the noise signal does not affect the speech signal uniformly over the entire spectrum. An improved Wiener filtering method is proposed in this paper for reducing background noise from speech signal in colored noise environments. In view of nonlinear variation of human ear sensibility in frequency spectrum, nonlinear multi-band Bark scale frequency spacing approach is used. The cross-correlation between the speech and noise signal is considered in the proposed method to reduce colored noise. To overcome harmonic distortion introduced in enhanced speech, in the proposed method regenerate the suppressed harmonics are regenerated. Objective and subjective tests were carried out to demonstrate improvement in the perceptual quality of speeches by the proposed technique.

Improved Multistage Wiener Filters in Nonhomogeneous Clutter Environments

A new method combining space-time preprocessing with multistage Wiener filters（STPMWF）is proposed to improve the performance of space-time adaptive processing（STAP）in nonhomogeneous clutter scenario.The new scheme only requires the data from the primary range bin,thus it can suppress discrete interferers efficiently,without calculating the inverse of covariance matrix.Comparing to the original MWF approach,the proposed scheme can be regarded as practical solutions for robust and effective STAP of nonhomogeneous radar data.The theoretical analysis shows that our STPMWF is simple in implementation and fast in convergence.The numeric results by using simulated data exhibit a good agreement with the proposed theory.

Restoration of Noisy 1/f Family Fractal Signal Based on Haar Wavelets and Wiener Filter

In order to restore noisy fractal Brownian motion(FBM),discrete fractional gaussiannoise(DFGN) combined with noise increments is decomposed by Haar wavelets based on Mallatalgorithm.Considering the correlation of detail coefficients,a bank of Wiener filters are used to estimatethe detail coefficients to reconstruct DFGN considering the estimated approximation coefficients in thecoarsest scale in the minimum mean square sense.Then,the reconstructed DFGN is used to restore FBM.In the digital simulation,in light of the restoration mean square error,we show that the suppose that thecorrelation of detail coefficients and the approximation coefficients in the coarsest scale for any Hurstcould be avoided is unrealistic.Moreover,we calculate the estimation root mean square error of the hurstparameter of the restored FBM to show the validity of our algorithm.

Improvement of energy resolution of x-ray transition-edge sensor using K-means algorithm and Wiener filter

We develop an x-ray Ti/Au transition-edge sensor(TES)with an Au absorber deposited on the center of TES and improved its energy resolution using the K-means clustering algorithm in combination with Wiener filter.We firstly extract the main parameters of each recorded pulse trace,which are adopted to classify these traces into several clusters in the K-means clustering algorithm.Then real traces are selected for energy resolution analysis.Following the baseline correction,the Wiener filter is used to improve the signal-to-noise ratio.Although the silicon underneath the TES has not been etched to reduce the thermal conductance,the energy resolution of the developed x-ray TES is improved from 94 eV to 44 eV at 5.9 keV.

An iterative Wiener filtering method based on the gravity gradient invariants

How to deal with colored noises of GOCE （Gravity field and steady - state Ocean Circulation Explorer） satellite has been the key to data processing. This paper focused on colored noises of GOCE gradient data and the frequency spectrum analysis. According to the analysis results, gravity field model of the optima] degrees 90-240 is given, which is recovered by COCE gradient data. This paper presents an iterative Wiener filtering method based on the gravity gradient invariants. By this method a degree-220 model was calculated from GOCE SGG （Satellite Gravity Gradient） data. The degrees above 90 of ITG2010 were taken as the prior gravity field model, replacing the low degree gravity field model calculated by GOCE orbit data. GOCE gradient colored noises was processed by Wiener filtering. Finally by Wiener filtering iterative calculation, the gravity field model was restored by space-wise harmonic analysis method. The results show that the model＇s accuracy matched well with the ESA＇s （European Space Agency） results by using the same data,

用Wiener滤波方法提取台站接收函数

本文提出了一种在时间域用Wiener滤波方法提取台站接收函数的方法 ,用远震P波波形的垂直分量为输入 ,接收函数作为滤波因子 ,远震P波波形的径向和切向分量作为期望输出 ,通过期望输出与实际输出的均方误差达极小 ,来提取接收函数。接收函数的计算可归结为Toeplitz方程的求解 ,可以采用Levinson递推算法。Toeplitz方程的非奇异性保证了Wiener滤波反褶积方法的稳定性。合成地震图与观测地震图的检验表明 ,用Wiener滤波方法测定台站接收函数是一种有效的时间域反褶积方法。

基于小波变换的Wiener滤波方法在农产品图像去噪中的应用

农产品图像的去噪是农产品图像处理中最基本、最重要的工作之一。为了更有效地去除农产品图像中的噪声。受二维离散Wiener滤波器计算方法的启发,提出了一种基于小波变换的Wiener滤波方法。该方法采用小波变换和Wiener滤波相结合的方法,具有稀疏性、多分辨率、去相关性、选基灵活性和在MSE意义上对图像进行最优估计的优点。该方法首先对含噪农产品图像ano做第一次小波变换得到低频图像a1和水平、垂直和对角三方向的高频图像hd1、vd1及dd1;其次对低频图像a1做Wiener滤波得到a1w,再对3个高频图像分别做Wiener滤波并合成得到g1w;接着对低频的a1w和高频g1w做小波逆变换,得到滤波图像“a1w+g1w”。同时,考虑到噪声主要在高频部分,所以直接把低频的a1和高频g1w做小波逆变换,得到滤波图像“a1+g1w”。这是对含噪图像ano做第1次小波变换的情况,其第2次、第3次及第4次变换的情况与此类似。这样可以得到许多滤波图像,然后根据图像信噪比PSNR和视觉效果,最终确定去噪效果最好的农产品图像。该方法应用于红枣、小麦杂草等农产品图像的去噪中,结果PSNR为158.23(视觉效果清晰),好于邻域平均法(PSNR为154.14)、中值滤波法(PSNR为155.82)、数学形态学(PSNR为154.07,视觉效果偏黑)、高斯滤波法(PSNR为153.79,视觉效果太黑)、直接维纳去噪(PSNR为154.14)和小波去噪(PSNR为158.18)等多种方法。试验结果表明,基于小波变换的Wiener滤波方法应用于农产品图像去噪具有信噪比高、视觉效果好等优点;基于小波变换的Wiener滤波方法用于农产品图像去噪是有效的、可行的。

基于小波变换的Wiener滤波算法去除苹果图像噪声

在小波变换的基础上,提出了一种基于小波变换的Wiener滤波去噪方法。采用该方法对苹果图像的加性噪声(高斯白噪声)去噪,结果PSNR为184.94,视觉效果清晰(而含噪声图像的PSNR为158.23,噪声太多不清晰),好于邻域平均法(PSNR为174.15,视觉上含部分噪声)、中值滤波法(PSNR为182.42)、小波阈值去噪(PSNR为171.59,视觉上也含部分噪声)和Wiener滤波去噪法(PSNR为173.65)的去噪结果,更好于数学形态学的去噪结果(PSNR为150.46,视觉上含较多噪声)。试验结果表明,基于小波变换的Wiener滤波方法对苹果图像加性噪声的去噪效果具有信噪比高、视觉效果好等优点。

离焦模糊数字图像的Wiener滤波频域复原

离焦模糊图像的退化模型可用均匀分布的圆盘函数表示 ,其对应的圆盘半径是需辨识的退化模型参数 (即模糊半径 )。利用模糊图像的频率域中的零点位置来估计模糊半径 ,采用简化Wiener滤波对模糊图像进行复原。实验结果表明该方法能够以较少的运算时间代价获取较好的复原效果 。

应用自解卷积和增量Wiener滤波实现迭代盲图像复原

提出了一种基于自解卷积和增量Wiener滤波的迭代盲图像复原算法(SDIWF-IBD)。将自解卷积点扩散函数估计法应用于迭代盲目反卷积,准确估计了点扩散函数频域;在图像估计时使用增量Wiener滤波,确保算法稳定收敛。为进一步控制算法收敛速度,引入内迭代加速方法,有效减少了算法的外部迭代次数。实验结果表明,复原后图像细节明显增加且失真小,算法快速收敛于较小误差。该算法复原效果良好,收敛快速可控,有利于实时应用。

基于Wiener滤波的小目标检测方法

红外云背景中小目标检测是红外成像技术的一种典型应用。为了检测红外云背景中的弱小目标,分析了背景特性,建立了红外场景模型,提出了一种基于Wiener滤波的自适应背景抑制方法。经过去除虚假目标和目标分割,再利用目标运动的连续性和规则性进行目标检测和跟踪。对包含了仿真目标的红外云背景图像序列进行了实验,结果表明,该方法能够有效地从信噪比为2.0的背景中检测到小目标。

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.

Wiener滤波器分解织物图像在织物疵点自动检测中的应用

首先介绍了 Wiener滤波器的算法和织物图像经 Wiener滤波后的分解 ,然后对分解后的经向和纬向子图像划分为 6 4个矩形块 ,对每个矩形块提取灰度极差作为特征值 ,再对特征值进行分析以实现对疵点的自动检测。实验证明了该方法的简单有效性 ,对素色织物的常见疵点具有快速。

基于Wiener滤波,K-L变换和BP网络的数字识别

研究基于传统模板匹配的识别灰度图像中数字的方法 .在对大量样本图像模板进行 Wiener滤波的基础上 ,利用 K- L 变换进行特征提取 ,用低维子空间描述高维空间中的图像 .将低维子空间中的向量加载到 BP网络的输入端进行训练 ,调整神经网络权值 .权值稳定后 ,在网络输入端加载经过预处理的待识别灰度数字图像 ,在输出端即可得到识别结果 .该方法有效地利用了 Wiener滤波器以最小方差对原始信号的恢复能力、K- L 变换的降维特性和

行阶梯观测矩阵下语音压缩感知观测序列的Volterra+Wiener模型研究

针对压缩感知理论下,语音信号经随机高斯矩阵投影后得到的观测序列随机性太强,难以建模的问题,提出了一种基于行阶梯观测矩阵的语音压缩感知观测序列的Volterra模型,利用该模型实现对语音压缩感知观测序列的预测,研究了Volterra滤波器输入维数与阶数对预测效果的影响,并利用维纳滤波器进一步降低预测误差。在相同的已知数据量下,基于部分压缩感知观测序列、Volterra模型、Wiener滤波器的重构,获得了优于高斯随机观测序列的重构性能。模型的研究为压缩感知与语音技术的结合提供一定的参考价值。

结合结构张量和Wiener滤波的图像去噪算法

利用图像的结构张量表示的局部结构信息,结合Wiener滤波给出了一种图像去噪算法,并采用了加性算子分解格式求解非线性扩散方程以提高计算效率。实验结果表明,该算法在去除噪声的同时很好地保持了图像中的强弱边界等细节特征,去噪后的图像在峰值信噪比和主观视觉效果上都有显著提高。