基于VMD与共振稀疏分解的舰船辐射噪声窄带特征提取

为了获取实测舰船辐射噪声信号中有效的目标信息、提高低信噪比条件下目标信号的可分性,文章提出了结合变分模态分解(Variational Mode Decomposition,VMD)和共振稀疏分解(Resonance-based Sparsity Signal Decomposition,RSSD)的舰船辐射噪声信号特征提取方法。基于舰船辐射噪声信号具有一定的周期性而外界干扰具有随机性的特点,首先利用VMD自相关分析的方法重构信号,主要剔除带外噪声分量;然后采用RSSD算法基于信号共振属性的不同,进一步滤除带内噪声和瞬态干扰,实现对信号中周期性振荡成分的提取;最后提取信号的波形结构特征用于目标的分类识别。仿真信号与实测信号分析表明,该方法可以较好地滤除带内外噪声,增强舰船辐射噪声信号固有的窄带特征。多类舰船目标的分类实验结果表明,该方法可以有效提高低信噪比信号的可分性,有利于提高目标识别的性能。     

Image Denoising with Adaptive Weighted Graph Filtering

Graph filtering, which is founded on the theory of graph signal processing, is proved as a useful tool for image denoising. Most graph filtering methods focus on learning an ideal lowpass filter to remove noise, where clean images are restored from noisy ones by retaining the image components in low graph frequency bands. However, this lowpass filter has limited ability to separate the low-frequency noise from clean images such that it makes the denoising procedure less effective. To address this issue, we propose an adaptive weighted graph filtering (AWGF) method to replace the design of traditional ideal lowpass filter. In detail, we reassess the existing low-rank denoising method with adaptive regularizer learning (ARLLR) from the view of graph filtering. A shrinkage approach subsequently is presented on the graph frequency domain, where the components of noisy image are adaptively decreased in each band by calculating their component significances. As a result, it makes the proposed graph filtering more explainable and suitable for denoising. Meanwhile, we demonstrate a graph filter under the constraint of subspace representation is employed in the ARLLR method. Therefore, ARLLR can be treated as a special form of graph filtering. It not only enriches the theory of graph filtering, but also builds a bridge from the low-rank methods to the graph filtering methods. In the experiments, we perform the AWGF method with a graph filter generated by the classical graph Laplacian matrix. The results show our method can achieve a comparable denoising performance with several state-of-the-art denoising methods.     

适于泥石流除噪的EMD联合小波阈值除噪方法

次声传感器采集到的泥石流次声信号中包含有大量的无关干扰信号,严重影响信号的分析与评估。针对含噪泥石流信号中无法准确确定噪声频段的特点,以及传统经验模态分解(Empirical Mode Decomposition, EMD)联合小波阈值去噪方法无法智能分辨噪声所在频段的缺点,提出了信号经EMD分解后,基于相关性选择噪声频段的方法。首先利用EMD分解获取信号的固有模态函数(Intrinsic Mode Function, IMF)分量,然后计算各个IMF分量与原始信号的相关性,根据相关性大小确定IMF噪声频段,然后采用小波阈值去噪方法对噪声频段进行处理,最后对处理后的信号进行重构得到去噪泥石流信号。通过模拟实验分析,证明该方法具有智能选择噪声频段的能力,是一种更适于泥石流信号的去噪方法。     

基于LCD-LLTSA的电动汽车电机轴承故障特征频率提取

为有效提取出电动汽车电机轴承故障特征频率,将局部特征尺度分解、线性局部切空间排列和包络分析进行结合,用于电动汽车电机轴承的故障特征频率的提取。首先利用局部特征尺度分解对电动汽车电机轴承故障信号进行分解,得到若干个内禀尺度分量;然后利用线性局部切空间排列对由内禀尺度分量构成的矩阵进行降维处理,得到低维矩阵并以此进行信号重构;最后对重构信号进行包络谱分析,获得故障特征频率。仿真信号和实验信号的实验结果验证了方法的有效性。     

Noise reduction for doppler ultrasound signal based on the adapted local cosine transform and the garrote thresholding method

In this paper, a novel approach, using the adapted local cosine transform combined with the non-negative garrote thresholding, is proposed to remove noise from the Doppler ultrasound signal. In the proposed approach, the local cosine transform is first performed on the signal of interest followed by a search algorithm to select the best basis. Then the coefficients of the obtained best basis are thresholded based on the non-negative garrote thresholding method. By means of the thresholded coefficients of the best basis, the signal is reconstructed. In the simulation study, the estimation precisions of the mean frequency waveform and the spectral width waveform are studied for the signal after denoising. The simulation and clinical results have shown that the proposed approach is superior to ones based on the wavelet transform, especially under low signal-to-noise ratio (SNR) circumstances.     

Attenuating noise from computed tomography medical images using a coefficients‐driven total variation denoising algorithm

The aim of image denoising is to recover a visually accepted image from its noisy observation with as much detail as possible. The noise exists in computed tomography images due to hardware errors, software faults and/or low radiation dose. Because of noise, the analysis and extraction of accurate medical information is a challenging task for specialists. Therefore, a novel modification on the total variational denoising algorithm is proposed in this article to attenuate the noise from CT images and provide a better visual quality. The newly developed algorithm can properly detect noise from the other image components using four new noise distinguishing coefficients and reduce it using a novel minimization function. Moreover, the proposed algorithm has a fast computation speed, a simple structure, a relatively low computational cost and preserves the small image details while reducing the noise efficiently. Evaluating the performance of the proposed algorithm is achieved through the use of synthetic and real noisy images. Likewise, the synthetic images are appraised by three advanced accuracy methods –Gradient Magnitude Similarity Deviation (GMSD), Structural Similarity (SSIM) and Weighted Signal‐to‐Noise Ratio (WSNR). The empirical results exhibited significant improvement not only in noise reduction but also in preserving the minor image details. Finally, the proposed algorithm provided satisfying results that outperformed all the comparative methods.     

全变分自适应图像去噪模型

通过分析三种主要变分去噪模型(调和、全变分以及广义全变分模型)的优缺点,提出了一种基于全变分的自适应图像去噪模型。该模型根据噪声图像的信噪比,采用高斯滤波器对图像进行预处理,克服了全变分模型引入的阶梯效应;利用图像中每一像素点的梯度信息,自适应选取去噪模型中决定扩散强弱的参数p(x,y),使接近边缘处平滑较弱,远离边缘处平滑较强。数值实验表明,本方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪性能,其峰值信噪比(PSNR)在高噪声水平下,较其他变分方法至少提高1.0dB左右。     

基于HSI颜色空间噪声检测的彩色图像去噪算法

目的为了有效去除彩色图像中的椒盐噪声,提高彩色图像质量。方法采用椒盐噪声检测和中值滤波相结合的方法,提出一种基于HSI颜色空间噪声检测的彩色图像去噪算法。将图像转换到HSI颜色空间,根据椒盐噪声在S通道具有极大值或极小值的特点判断出可疑椒盐噪声的位置,在H通道、I通道将可疑椒盐噪声分为噪声点和有用信号,对检测出的噪声像素进行中值滤波去噪。结果采用文中算法去噪后,验证图像主观评价值(Z)为1.30,平均PSNR为37.54,SSIM为0.99,Entropy为7.31,在主客观评价上优于现在常用算法。结论文中提出算法可以为彩色图像椒盐噪声的去噪提供理论基础,具有一定的实际应用价值。     

A filter for on-line estimation of spectral content

A robust filter algorithm to extract, a posteriori, the rational signal model from a noisy measurement, with little a priori information, is proposed. The spectrum and the statistics of the signal and of the corrupting noise are assumed unknown, except that the signal is assumed to have a rational spectrum. An algorithm based on system and signal theory is derived to select a set of frequencies where the signal-to-noise ratio (SNR) is high from a given measurement spectrum. The density of selected frequencies weights the importance of the measurement as a function of frequency, An estimate of the signal model is obtained from the best weighted least-squares fit to the measurement spectrum at the selected frequencies. The proposed filter has applications to control and signal processing, and a wide variety of applications are presented. Applications include: system identification of a dc motor and a two-link manipulator, extraction of a myo-electric signal from a noisy measurement, the assignment of a rational model to a vegetation tissue's impedance, and to the number density profile of atmospheric oxygen     

基于时频谱图的脉冲噪声抑制方法

从时频分析的角度,提出了一种新的音频信号脉冲噪声的处理方法。该方法基于被污染信号的时频谱图,通过区分纯净信号和脉冲噪声信号的频域特性与相关性来检测脉冲噪声。首次提出前后信息相关联的"限幅"噪声抑制方法,并利用带过滤系统的中值滤波方法分别对短时和暂态两种脉冲噪声信号加以抑制和消除。和信噪比相比,还进一步提出了四个指标专门用于评价去除脉冲噪声方法的性能。基于这四个指标,分析了如何调整参数以获得更好的检测和修复性能,并用大量仿真实验证实了这种新方法的有效性。最后给出了系统仿真结果,并指出了该方法的应用前景。     

微弱裂纹信号的稀疏编码提取

针对重大技术装备中关键基础部件早期裂纹信号提取困难这一问题,提出一种基于独立分量分析（ICA）的稀疏编码收缩（SCS）去噪方法,即采用泛化高斯模型（GGM）在ICA空间中估计信号独立系数的概率密度函数（PDF）,并利用最大后验（MAP）估计方法进行非线性去噪的微弱信号提取方法。通过对不同信噪比的含噪微弱裂纹信号的提取研究,结果表明,此方法能提取出输入信噪比低于-27dB的微弱信号,且波形与频谱均能较好的和原信号保持一致。同时,其去噪效果远远好于小波降噪方法,是一种较好的微弱信号提取方法。     

Denoising in digital speckle pattern interferometry using fast discrete curvelet transform

In digital speckle pattern interferometry, the denoising of speckle fringe patterns is of vital importance for quantitative extraction of phase distribution. A filtering method of fast discrete curvelet transform based on weighted average thresholding technique is proposed in this paper for noise removal in speckle fringe patterns. Both computer-simulated and experimental digital speckle pattern interferometry fringe patterns are adopted to evaluate the performance of the proposed filtering method. In addition, a widely used and representative filtering method, windowed Fourier filter, is introduced for making a comparison and validation in the image processing effect, and the parameter of peak signal noise ratio is also used for assessment of denoising effect. It is shown from the filtered results that the filtering method of fast discrete curvelet transform is effecitve to remove speckle noises and simultaneously preserve fringe structure information.     

An extremely fast adaptive high-performance filter to remove salt and pepper noise using overlapping medians in images

A new fast adaptive high-performance filter (FAHPF) has been proposed to remove salt-and-pepper noise in images. ‘Maximize the speed without compromising denoising performance’ is the fundamental intention to build up the FAHPF algorithm. Among diverse phases of filtering employed in the FAHPF, overlapping medians (OM) is our newly proposed frame-based filtering concept which is the basis for speed of FAHPF and running averages embedded with OM is an idea behind excellent denoising performance of FAHPF at the same pace. Simulation experiments have been conducted and denoising results of FAHPF has been investigated against very recently developed filtering methods. It is proved that the FAHPF excellently outperforms many of state-of-the-art filters considered for comparison, in terms of peak signal to noise ratio, structural similarity index, and visual representation and requires the extremely shortest execution time among all, which could make it as a real time filter.     

采用离散余弦变换的小波图像去噪方法

提出一种通过对小波域中噪声能量的估计来进行去噪的新方法。算法采用离散余弦变换(DCT)提取小波系数的主要特征,无需对噪声方差进行估计。对图像进行小波分解,利用 DCT对高频子带进行局部特征提取;利用部分 DCT 系数对小波系数进行重建,并以重建系数的平均能量作为局部噪声能量的估计;去除原小波系数中的噪声分量后,进行小波逆变换,得到去噪后的图像。实验证明,其峰值信噪比(PSNR)比通常的阈值萎缩法提高了 2-4dB。     

一种基于奇异值分解的自适应降噪方法

根据信号处理基本理论和方法．针对奇异值分解方法中有关的Hankel矩阵有效秩难以确定的难题,提出了一种奇异值分解方法,即主分量分解方法．并通过试验数据进行了验证。仿真信号和海上实录信号的降噪实验研究表明．提出的方法比基本的LMS滤波和奇异值分解降噪效果更加优越,能有效提高信噪比并去除噪声。     

基于熵-流特征和樽海鞘群优化支持向量机的故障诊断方法

针对旋转机械设备故障特征提取困难的问题,提出一种熵-流特征和樽海鞘群优化支持向量机(salp swarm optimization support vector machine,SSO-SVM)的故障诊断方法。利用改进多尺度加权排列熵(improved multiscale weighted permutation entropy,IMWPE)提取机械设备不同工况下的故障特征;采用监督等度规映射(S-Isomap)流形学习进行降维处理,获取低维的熵-流特征集;将熵-流特征输入至SSO-SVM多故障分类器进行识别与诊断。行星齿轮箱故障诊断实验分析结果表明:IMWPE+S-Isomap熵-流特征提取方法优于现有的多尺度排列熵(multiscale permutation entropy,MPE)、多尺度加权排列熵(multiscale weighted permutation entropy,MWPE)和IMWPE等熵值特征提取方法以及IMWPE+等度规映射(Isomap)和IMWPE+线性局部切空间排列(linear local tangent space alignment,LLTSA)等熵-流特征提取方法;樽海鞘群算法对支持向量机参数寻优效果优于粒子群、灰狼群、人工蜂群和蝙蝠群等算法;所提故障诊断方法识别精度达到100%,能够有效诊断出行星齿轮箱各工况类型。     

Quality enhancement of image generated with bistatic ground based noise waveform SAR

A new method for quality enhancement in a noise synthetic aperture radar (SAR) image and the first results of its application to the SAR image generated with the use of a bistatic Ka-band ground-based noise waveform SAR (GB NW-SAR) are presented. A SAR image generated with a noise SAR suffers from the masking effect which is tied to residual random fluctuations in noise radar response from bright scatterers in the scene. This is similar to the masking effect present in the deterministic waveform SAR when the signal sidelobes of echoes from bright scatterers may mask the main response from a weaker target. The procedure presented is a variation of the CLEAN algorithm. Knowing precisely the emitted signal and finding positions of the strongest scatterers one may model the echo signal originated from a selected scatterer. Extraction of the modelled signal from the received one reduces the residual fluctuations and makes it possible to clean the image and increase its dynamic range. The final image is constructed from the cleaned signal and the previously removed strongest scatterers. A theoretical background is provided to the proposed procedure and its application to enhance the SAR image using simulated data as well as data generated by the Ka-band bistatic GB NW-SAR is demonstrated. The GB NW-SAR, recently developed and tested in LNDES IRE NASU, may operate in CW and pulse random signal regimes for short range applications.     

Fusion of noisy images based on joint distribution model in dual-tree complex wavelet domain

Source images are frequently corrupted by noise before fusion, which will lead to the quality decline of fused image and the inconvenience for subsequent observation. However, at present, most of the traditional medical image fusion scheme cannot be implemented in noisy environment. Besides, the existing fusion methods scarcely make full use of the dependencies between source images. In this research, a novel fusion algorithm based on the statistical properties of wavelet coefficients is proposed, which incorporates fusion and denoising simultaneously. In the proposed algorithm, the new saliency and matching measures are defined by two distributions: the marginal statistical distribution of single wavelet coefficient fit by the generalized Gaussian Distribution and joint distribution of dual source wavelet coefficients modeled by the anisotropic bivariate Laplacian model. Additionally, the bivariate shrinkage is introduced to develop a noise robust fusion method, and a moment-based parameter estimation applied in the fusion scheme is also exploited in denoising method, which allows to achieve the consistency of fusion and denoising. The experiments demonstrate that the proposed algorithm performs very well on both noisy and noise-free images from multimodal medical datasets (computerized tomography, magnetic resonance imaging, magnetic resonance angiography, etc.), outperforming the conventional methods in terms of both fusion quality and noise reduction.     

一种改进的窄带信号降噪算法及其应用

提出一种改进的基于FFT窄带信号频域降噪算法,并研究了其在宽带信号分段滤波中的应用。针对传统的基于FFT窄带信号频域去噪方法的不足,首先估计出信号频率与量化频率点的偏离程度,然后对信号进行频移,使信号频率尽量接近量化频率点,再进行频域滤波,并对滤波后信号逆向频移恢复原信号。最后,采用这种基于频移的窄带信号降噪算法对宽带信号进行分段滤波处理。Matlab仿真表明,改进的算法在不同的频率段内性能,且明显优于传统的窄带信号频域去噪算法。     

动态载荷时域识别的联合去噪修正和正则化预优迭代方法

系统响应可表示为单位脉冲响应函数与激励载荷的卷积,将其离散化一组线性方程组,则载荷识别问题即转化为求解线性方程组的反问题。针对响应中带有噪音时载荷识别的困难,提出了联合奇异熵去噪修正和正则化预优的共轭梯度迭代识别方法。一方面对含噪信号进行基于奇异熵的去噪处理,提高反问题求解中输入数据的精度。另一方面利用正则化方法对共轭梯度迭代算法进行预优,改善反问题的非适定性。由于从输入的响应数据去噪和正则化算法两方面同时改善动态载荷识别反问题的求解,因此可以有效地抑制噪声,提高识别精度。通过数值算例分析,表明在不同的噪声水平干扰下,其识别精度均优于常规的正则化方法,能够实现有效稳定地识别动态载荷。最后通过实验研究进一步验证了该方法的正确性和有效性。