一种基于Non-Local Means的地基差分干涉雷达相位滤波改进方法

地基差分干涉雷达相位图中,往往含有大量相位噪声,严重影响相位解缠和形变测量结果.有鉴于此,本文提出一种改进的自适应非局部均值(Non-Local Means)组合滤波算法.该算法首先利用相干系数构造出可自适应的平滑参数模型,有效改善了Non-Local Means算法在滤波参数选择上的固定性.其次,利用维纳滤波可对空变...     

基于沃尔泰拉理论的集总光纤非线性噪声补偿

通过沃尔泰拉(Volterra)级数理论求得了非线性薛定谔方程(NLSE)的半解析解,在考虑光纤损耗、色散及非线性效应的情况下,推出了长距离在线级联掺铒光纤放大器(EDFA)光纤通信系统中信号和自发辐射噪声(ASE)之间耦合串扰的半解析表达式,得到了多跨距传输接收端输出信号和发射端输入信号之间的关系式.根据维纳(Wiener)滤波理论的时域滤波原理,在多跨距传输系统接收端设计了对非线性乘性噪声有滤波作用的维纳滤波器,并对预集总色散补偿、后集总色散补偿、分布链路色散补偿系统及一对三"跨距成比例平移对称的色散非线性同步补偿系统进行了仿真模拟研究.结果表明了提出的设计思路及方法的可行性,为进一步提高传输距离增大入纤功率提供了新的思路.     

Efficient multiframe Wiener restoration of blurred and noisy imagesequences

Computationally efficient multiframe Wiener filtering algorithms that account for both intraframe (spatial) and interframe (temporal) correlations are proposed for restoring image sequences that are degraded by both blur and noise. One is a general computationally efficient multiframe filter, the cross-correlated multiframe (CCMF) Wiener filter, which directly utilizes the power and cross power spectra of only NxN matrices, where N is the number of frames used in the restoration. In certain special cases the CCMF lends itself to a closed-form solution that does not involve any matrix inversion. A special case is the motion-compensated multiframe (MCMF) filter, where each frame is assumed to be a globally shifted version of the previous frame. In this case, the interframe correlations can be implicitly accounted for using the estimated motion information. Thus the MCMF filter requires neither explicit estimation of cross correlations among the frames nor matrix inversion. Performance and robustness results are given.     

一种小波域与空域相结合的图像滤波方法

利用小波阈值去噪方法和传统空间域Lee 滤波的特点, 提出了一种图像去噪的的组合滤波方案。首先在小波域对图像阈值去噪, 得到预去噪图像; 再在空间域上利用自适应Wiener 滤波器进一步提高恢复图像的精度。为了保证小波域和空间域两种算法之间的匹配, 对预去噪图像中残留噪声的分布进行了研究, 对其噪声方差估计做了改进, 提出了一种估计噪声方差的近似最优公式。仿真实验表明, 与单独的在小波域或空域去噪相比, 该方法的均方误差和信噪比指标均得到了改善。     

Noise adaptive soft-switching median filter

Existing state-of-the-art switching-based median filters are commonly found to be nonadaptive to noise density variations and prone to misclassifying pixel characteristics at high noise density interference. This reveals the critical need of having a sophisticated switching scheme and an adaptive weighted median filter. We propose a novel switching-based median filter with incorporation of fuzzy-set concept, called the noise adaptive soft-switching median (NASM) filter, to achieve much improved filtering performance in terms of effectiveness in removing impulse noise while preserving signal details and robustness in combating noise density variations. The proposed NASM filter consists of two stages. A soft-switching noise-detection scheme is developed to classify each pixel to be uncorrupted pixel, isolated impulse noise, nonisolated impulse noise or image object's edge pixel. "No filtering" (or identity filter), standard median (SM) filter or our developed fuzzy weighted median (FWM) filter will then be employed according to the respective characteristic type identified. Experimental results show that our NASM filter impressively outperforms other techniques by achieving fairly close performance to that of ideal-switching median filter across a wide range of noise densities, ranging from 10% to 70%     

基于均值漂移的自适应滤波及其在光谱信号处理中的应用

该文给出了一种基于均值漂移的自适应双边滤波方法,其性能仅取决于空域的核尺度参数,幅度域的核尺度是根据信号的局部特征自适应选取的。该方法能够去除脉冲噪声,能有效抑制非脉冲噪声,并有较强的边缘保护能力。实验和分析表明本文方法的整体性能优于高斯滤波和中值滤波。该文将所提出方法用于天体光谱的去噪,并与均值漂移滤波和小波硬阈值法进行了比较,结果表明：该方法能够有效抑制光谱中天光背景噪声和随机噪声,并能较好地保护谱线信息,更适于天体光谱信号的处理。     

Adaptive time-delay estimation in nonstationary signal and/or noisepower environments

A model for an adaptive time-delay estimator is proposed to improve its performance in estimating the difference in arrival time of a bandlimited random signal received by two spatially separated sensors in an environment where the signal and noise power are time varying. The system comprises two adaptive units: a filter to compensate time shift between the two receiver channels and a gain control to provide Wiener filtering. Both the filter coefficients and the variable gain are adjusted simultaneously by using modifications from the stochastic mean-square-error gradient in the traditional adaptive least-mean-square time-delay estimation (LMSTDE) method. The convergence characteristics of the proposed system are analyzed in detail and compared with those obtained by the traditional technique. Theoretical results show that, unlike the LMSTDE configuration, this arrangement can decouple the adaptation of time shift from the changing signal and/or noise power, which in turn gives rise to better convergence behavior of the delay estimate. Simulation results are included to illustrate the effectiveness of the new model and corroborate the theoretical developments     

Adaptive restoration of textured images with mixed spectra

We consider the adaptive restoration of inhomogeneous textured images, where the individual regions are modeled using a Wold-like decomposition. A generalized Wiener filter is developed to accommodate mixed spectra, and unsupervised restoration is achieved by using the expectation-maximization (EM) algorithm to estimate the degradation parameters. This algorithm yields superior results when compared with supervised Wiener filtering using autoregressive (AR) image models.     

On-line estimation of signal and noise parameters with application to adaptive Kalman filtering

This paper presents two algorithms for on-line estimation of the optimal gain of the Kalman filter applied to sensor signals when the signal-to-noise ratio is unknown. First-order spectra of a pure signal and colored measurement noise have been assumed. The proposed adaptive Kalman filtering algorithms have been tested for various spectra of the pure signal and noise, and for various signal-to-noise ratios. The effect of the length of an adaptation step and a sampling frequency on the mean square errors of the pure signal estimation has also been examined. Although the test have been performed for stationary signals, the algorithms presented can also be used successfully for time-varying sensor signals when the signal-to-noise ratios vary very slowly in comparison with the length of the adaptation step.The results are helpful for designers who synthesize optimal linear digital filters for sensor signals with first-order spectra and colored measurement noise. The estimation error curves presented enable designers to determine the noise reduction attainable for particular applications of the adaptive Kalman filtering algorithms.     

基于迭代滤波盲复原算法的水下图像噪声去除

摘要：图像复原的目的是从观测到的退化图像重建原始图像,维纳滤波与约束去卷积滤波是比较常采用的复原方法。在未知降质函数的情况下,直接运用维纳滤波和约束去卷积滤波有一定困难。针对此提出以维纳滤波与约束去卷积滤波为模型的迭代滤波盲复原算法对水下图像进行去噪。实验证明,该方法获得了比较理想的复原效果。     

Split Wiener filtering with application in adaptive systems

This paper proposes a new structure for split transversal filtering and introduces the optimum split Wiener filter. The approach consists of combining the idea of split filtering with a linearly constrained optimization scheme. Furthermore, a continued split procedure, which leads to a multisplit filter structure, is considered. It is shown that the multisplit transform is not an input whitening transformation. Instead, it increases the diagonalization factor of the input signal correlation matrix without affecting its eigenvalue spread. A power normalized, time-varying step-size least mean square (LMS) algorithm, which exploits the nature of the transformed input correlation matrix, is proposed for updating the adaptive filter coefficients. The multisplit approach is extended to linear-phase adaptive filtering and linear prediction. The optimum symmetric and antisymmetric linear-phase Wiener filters are presented. Simulation results enable us to evaluate the performance of the multisplit LMS algorithm.     

Relative importance of the error sources in Wiener restoration of scintigrams

Through simulation studies, the relative importance of three error sources in Wiener filtering as applied to scintigrams is quantified. The importance of these error sources has been quantified using the percentage changed in squared error (compared to that of an image restored using an ideal Wiener filter) which is caused by estimating one of three factors in the Wiener filter. Estimating the noise power spectrum using the total image count produced to appreciable change in the squared error (less than 1%). Estimating the power spectrum of the true image from that of the degraded image produced small to moderate increases in the squared error (4-139%). In scintigraphic imaging, the modular transfer function (MTF) is dependent on source depth; hence, this study underscores the importance of using methods which reduce the depth dependence of the effective MTF prior to applying restoration filters. A novel method of estimating the power spectrum of the true image from that of the degraded images is also described and evaluated. Wiener restoration filters based on this spectral estimation method are found to be competitive with the image-dependent Metz restoration filter.     

医学图像增强系统设计

为了改善医学图像的视觉效果,提高图像的清晰度,使之更适合于机器的分析处理以及人的视觉特性,并突出病灶点,为病理学诊断和临床诊断提供可靠依据。设计了一个对医学图像十分具有针对性的图像增强系统。针对CT图像的电子噪声提出了基于修正维纳滤波的小波包去噪算法;针对B型超声图像的散斑噪声提出了基于脉冲耦合神经网络(PCNN)模型的小波自适应斑点噪声滤除算法;针对医学图像对比度低,边缘信息模糊等特点,提出了基于小波变换的医学图像增强算法。当噪声方差为0.01时,基于脉冲耦合神经网络(PCNN)模型的小波自适应斑点噪声滤除算法获得的PSNR比经Wiener滤波方法获得的PSNR高出9 dB。系统能快速找到噪声点进行定点去噪,能有效提高医学图像的对比度,增强边缘细节信息,突出病灶点的位置,从而达到较好的处理效果,为医疗工作者观察病症提供更加清晰准确的依据。     

一种小波域与空域相结合的图像滤波方法

利用小波阈值去噪方法和传统空间域Lee滤波的特点,提出了一种图像去噪的的组合滤波方案。首先在小波域对图像阈值去噪,得到预去噪图像;再在空间域上利用自适应Wiener滤波器进一步提高恢复图像的精度。为了保证小波域和空间域两种算法之间的匹配,对预去噪图像中残留噪声的分布进行了研究,对其噪声方差估计做了改进,提出了一种估计噪声方差的近似最优公式。仿真实验表明,与单独的在小波域或空域去噪相比,该方法的均方误差和信噪比指标均得到了改善。     

Noise Removal From Hyperspectral Images by Multidimensional Filtering

A generalized multidimensional Wiener filter for denoising is adapted to hyperspectral images (HSIs). Commonly, multidimensional data filtering is based on data vectorization or matricization. Few new approaches have been proposed to deal with multidimensional data. Multidimensional Wiener filtering (MWF) is one of these techniques. It considers a multidimensional data set as a third-order tensor. It also relies on the separability between a signal subspace and a noise subspace. Using multilinear algebra, MWF needs to flatten the tensor. However, flattening is always orthogonally performed, which may not be adapted to data. In fact, as a Tucker-based filtering, MWF only considers the useful signal subspace. When the signal subspace and the noise subspace are very close, it is difficult to extract all the useful information. This may lead to artifacts and loss of spatial resolution in the restored HSI. Our proposed method estimates the relevant directions of tensor flattening that may not be parallel either to rows or columns. When rearranging data so that flattening can be performed in the estimated directions, the signal subspace dimension is reduced, and the signal-to-noise ratio is improved. We adapt the bidimensional straight-line detection algorithm that estimates the HSI main directions, which are used to flatten the HSI tensor. We also generalize the quadtree partitioning to tensors in order to adapt the filtering to the image discontinuities. Comparative studies with MWF, wavelet thresholding, and channel-by-channel Wiener filtering show that our algorithm provides better performance while restoring impaired HYDICE HSIs.     

Wavelet-based Rician noise removal for magnetic resonance imaging

It is well known that magnetic resonance magnitude image data obey a Rician distribution. Unlike additive Gaussian noise, Rician "noise" is signal-dependent, and separating signal from noise is a difficult task. Rician noise is especially problematic in low signal-to-noise ratio (SNR) regimes where it not only causes random fluctuations, but also introduces a signal-dependent bias to the data that reduces image contrast. This paper studies wavelet-domain filtering methods for Rician noise removal. We present a novel wavelet-domain filter that adapts to variations in both the signal and the noise.     

Robust Wiener filtering for multiple inputs with channel distortion (Corresp.)

Robust Wiener filtering has previously been considered for the single-input (scalar) case where there is no channel distortion and where the signal to be estimated is the source signal itself. Here, these results are extended to the multiple-input (vector) case where linear channel distortion is allowed and the signal to be estimated is a linear-filtered version of the source signal. The results are obtained from those for the single-input ease by modifying the constraints on signal and noise characteristics. Such a modification is motivated by examining the expression of the mean-squared error for the optimum filter.     

Gaussian Noise Filtering from ECG by Wiener Filter and Ensemble Empirical Mode Decomposition

Empirical mode decomposition (EMD) is a powerful algorithm that decomposes signals as a set of intrinsic mode function (IMF) based on the signal complexity. In this study, partial reconstruction of IMF acting as a filter was used for noise reduction in ECG. An improved algorithm, ensemble EMD (EEMD), was used for the first time to improve the noise-filtering performance, based on the mode-mixing reduction between near IMF scales. Both standard ECG templates derived from simulator and Arrhythmia ECG database were used as ECG signal, while Gaussian white noise was used as noise source. Mean square error (MSE) between the reconstructed ECG and original ECG was used as the filter performance indicator. FIR Wiener filter was also used to compare the filtering performance with EEMD. Experimental result showed that EEMD had better noise-filtering performance than EMD and FIR Wiener filter. The average MSE ratios of EEMD to EMD and FIR Wiener filter were 0.71 and 0.61, respectively. Thus, this study investigated an ECG noise-filtering procedure based on EEMD. Also, the optimal added noise power and trial number for EEMD was also examined.     

自适应维纳滤波在钢水红外图像去噪中的应用

红外测温系统的应用减少了人工测温的安全事故,但其温度的准确性取决于由红外热像仪获得的图像的质量.为了对钢水红外图像质量的影响,提出了基于自适应维纳滤波的去噪方法.通过自相关的参数指数衰减模型来控制算法的计算复杂性和敏感性,进而有效提高维纳滤波器的去降噪性能.基于对不同温度下钢水红外图像的去噪处理,验证了所提去噪方法比维...     

Second-order adaptive Volterra system identification based ondiscrete nonlinear Wiener model

The authors present the nonlinear LMS adaptive filtering algorithm based on the discrete nonlinear Wiener (1942) model for second-order Volterra system identification application. The main approach is to perform a complete orthogonalisation procedure on the truncated Volterra series. This allows the use of the LMS adaptive linear filtering algorithm for calculating all the coefficients with efficiency. This orthogonalisation method is based on the nonlinear discrete Wiener model. It contains three sections: a single-input multi-output linear with memory section, a multi-input, multi-output nonlinear no-memory section and a multi-input, single-output amplification and summary section. For a white Gaussian noise input signal, the autocorrelation matrix of the adaptive filter input vector can be diagonalised unlike when using the Volterra model. This dramatically reduces the eigenvalue spread and results in more rapid convergence. Also, the discrete nonlinear Wiener model adaptive system allows us to represent a complicated Volterra system with only few coefficient terms. In general, it can also identify the nonlinear system without over-parameterisation. A theoretical performance analysis of steady-state behaviour is presented. Computer simulations are also included to verify the theory