共查询到18条相似文献,搜索用时 140 毫秒
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有源噪声控制是一种主动控制方法,目前已广泛应用于对高斯分布噪声进行衰减。但是传统的用于控制噪声的自适应算法不再适用大多数服从非高斯分布的脉冲噪声,主要原因是这种脉冲噪声没有有限的二阶统计量。在经典的Filter-x LMS算法的基础上提出两种适用于服从非高斯分布尖峰脉冲噪声情况下的在线参数辨识方法,一种是利用在线参数辨识方法对服从S[α]S稳定分布的脉冲噪声进行特征指数的估计,进而实现降噪目的的FxLMPest和FxLMADadapt算法;另一种是在Sun等人提出的SKM和AM算法基础上利用在线递归过程实现对幅度阈值估计的BDP算法。这两种算法均不需要获得脉冲噪声的特征指数和阈值的先验信息,仿真分析结果表明这两种算法能有效抑制脉冲噪声,并且其鲁棒性明显好于Filter-x LMS算法。 相似文献
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边界噪声检测耦合差分曲率驱动的脉冲噪声图像降噪 总被引:1,自引:1,他引:0
目的提出边界识别噪声检测耦合差分曲率驱动扩散模型的脉冲噪声图像降噪算法,用于高密度(≥50%)脉冲噪声的消除。方法基于传统边界识别噪声检测BDND,定义噪声像素分类规则,设计新的边界识别噪声检测M-BDND机制;定位噪声边界,精确识别噪声像素点,形成噪声区域与完好区域;利用噪声像素点的周边信息形成掩码,对其进行修复,有效填补噪声像素点,而在完好区域只将像素点进行复制;构造了差分曲率驱动扩散模型控制噪声像素区域的扩散过程,完成图像复原,并对该模型进行了数值分析。结果与当前图像降噪技术相比,对于等密度随机值脉冲噪声而言,提出算法的误检率与虚警率更低;在噪声密度高达90%时,仍然具有可接受的降噪效果,能够更好地保留原图的边缘与细节特征,且复原图像的一维行距像更好,与真实图像的吻合程度高。结论该算法可用于高密度脉冲噪声的图像降噪处理。 相似文献
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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。 相似文献
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提出了一种星载合成孔径雷达数字成像抑制相干斑的算法--OMRD(Overlapped Multilook Range-Doppler)算法,该算法基于星载合成孔径雷达成像基本原理和传统的RD算法,通过距离向脉冲压缩,距离徙动校正,方位向合成孔径处理,去除相干斑噪声处理,实现了星载合成孔径雷达的数字成像,降低了相干斑噪声的影响,提高了图像信噪比.对ERS-1星载原始数据进行实验成像,得到了清晰的意大利罗马地区图像.实验结果表明,OMRD算法是一种有效的星载合成孔径雷达成像抑制相干斑的算法. 相似文献
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介绍一种将自适应噪声抵消算法应用于消除周期性工频脉冲干扰的方法。该方法利用周期sinc函数仿真工频脉冲干扰信号,与白噪声叠加作为参考输入,利用最小均方(Least Mean Square,LMS)算法与归一化最小均方(Normalized Least Mean Square,NLMS)算法进行自适应噪声抵消滤波仿真实验。MATLAB仿真处理结果显示,在无增益、增益饱和、增益过饱和这三种情况下,当信噪比为3 d B时,分别用LMS算法与NLMS算法滤波后可以清晰地分辨多次回波。 相似文献
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提出了一种基于时域加权的新的非叠代优化算法,用于重构经过采样示波器测量的阶跃信号、脉冲信号等。该算法构造了一个期望误差函数,由于这个期望误差函数与真实误差很接近,因此构造了新的价值函数,并使用时域加权来优化不同的波形,在减小时域振铃时有效的控制噪声放大。与误差能量,归一化方法相比,该算法经过仿真实验表明了其可行性和有效性。 相似文献
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一种噪声与振动主动控制的滤波-MLMS算法 总被引:3,自引:3,他引:0
滤波-LMS算法在噪声与振动主动控制中最为常用,但在具有冲击干扰的环境下,其收敛性能变差,本文提出一种更具鲁棒性的滤波-中值LMS算法,无论在冲击噪声还是在非冲击噪声环境下,都具有很好的收敛性,计算机仿真结果进一步验证了这种算法的有效性。 相似文献
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This paper addresses the problem of how to restore degraded images where
the pixels have been partly lost during transmission or damaged by impulsive noise. A
wide range of image restoration tasks is covered in the mathematical model considered
in this paper – e.g. image deblurring, image inpainting and super-resolution imaging.
Based on the assumption that natural images are likely to have a sparse representation
in a wavelet tight frame domain, we propose a regularization-based approach to
recover degraded images, by enforcing the analysis-based sparsity prior of images in a
tight frame domain. The resulting minimization problem can be solved efficiently by
the split Bregman method. Numerical experiments on various image restoration tasks
– simultaneously image deblurring and inpainting, super-resolution imaging and image
deblurring under impulsive noise – demonstrated the effectiveness of our proposed algorithm.
It proved robust to mis-detection errors of missing or damaged pixels, and
compared favorably to existing algorithms. 相似文献
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滤波-x最小均方(Filtered-x Least Mean Square,FxLMS)算法是主动噪声控制的经典算法,其存在收敛速度与稳态误差不可兼得的问题,解决方法之一是采用变步长FxLMS算法。总结了现有的基于误差非线性函数的变步长模型,并将其应用于FxLMS算法以改善算法性能。用三种常见的噪声作为参考输入信号进行仿真试验,对比了不同非线性函数变步长算法的性能。结果表明,变步长FxLMS算法能有效改善参考信号为高斯白噪声和正弦波时的收敛速度和稳态误差,且不同噪声环境下最优算法不同,但此类算法无法提升噪声源为冲击噪声时的性能。这为不同应用场景下算法的选取提供了参考。将变步长FxLMS算法应用于某车型的发动机主动噪声控制,结果表明,变步长FxLMS能显著提高定速工况的系统性能,但对急加速工况效果并不明显。 相似文献
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R. Pugalenthi A. Sheryl Oliver M. Anuradha 《International journal of imaging systems and technology》2020,30(4):1119-1131
Noise filtering performance in medical images is improved using a neuro-fuzy network developed with the combination of a post processor and two neuro-fuzzy (NF) filters. By the fact, the Sugeno-type is found to be less accurate during impulse noise reduction process. In this paper, we propose an improved firefly algorithm based hybrid neuro-fuzzy filter in both the NF filters to improve noise reduction performance. The proposed noise reduction system combines the advantages of the neural, fuzzy and firefly algorithms. In addition, an improved version of firefly algorithm called searching diversity based particle swarm firefly algorithm is used to reduce the local trapping problem as well as to determine the optimal shape of membership function in fuzzy system. Experimental results show that the proposed filter has proved its effectiveness on reducing the impulse noise in medical images against different impulse noise density levels. 相似文献
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Wasim Ullah Khan Yigang He Muhammad Asif Zahoor Raja Naveed Ishtiaq Chaudhary Zeshan Aslam Khan Syed Muslim Shah 《计算机、材料和连续体(英文)》2021,67(1):815-834
Abstract In this paper, a novel design of the flower pollination algorithm is presented for model identification problems in nonlinear active noise control systems. The recently introduced flower pollination based heuristics is implemented to minimize the mean squared error based merit/cost function representing the scenarios of active noise control system with linear/nonlinear and primary/secondary paths based on the sinusoidal signal, random and complex random signals as noise interferences. The flower pollination heuristics based active noise controllers are formulated through exploitation of nonlinear filtering with Volterra series. The comparative study on statistical observations in terms of accuracy, convergence and complexity measures demonstrates that the proposed meta-heuristic of flower pollination algorithm is reliable, accurate, stable as well as robust for active noise control system. The accuracy of the proposed nature inspired computing of flower pollination is in good agreement with the state of the art counterpart solvers based on variants of genetic algorithms, particle swarm optimization, backtracking search optimization algorithm, fireworks optimization algorithm along with their memetic combination with local search methodologies. Moreover, the central tendency and variation based statistical indices further validate the consistency and reliability of the proposed scheme mimic the mathematical model for the process of flower pollination systems. 相似文献
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为规避最小均方(Least Mean Square,LMS)算法不能同时提高收敛速度和降低稳态误差的固有缺陷,以及已有变步长LMS算法存在收敛速度慢和稳态误差估计精度差的问题,文中提出了一种基于变步长归一化频域块(Normalized Frequency-domain Block,NFB) LMS算法的汽车车内噪声主动控制方法。为了比较,应用传统的LMS算法、基于反正切函数的变步长LMS算法和变步长NFB-LMS算法分别进行实测汽车车内噪声的主动控制。结果表明,与其他两个算法相比,变步长NFB-LMS算法的收敛速度提高了70%以上,稳态误差减小了90%以上。变步长NFB-LMS算法在处理车内噪声信号时具有很高的效率,为进行汽车车内噪声主动控制提供了一种新方法。 相似文献
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在噪声主动控制系统中?,滤波-x递归最小二乘(FxRLS)算法收敛速度快但计算量大。本文提出了格型联合估计滤波器结构与基于QR分解的最小二乘格型(QRD-LSL)自适应滤波算法相结合的噪声控制方法,该方法对联合估计过程进行了改进并得到了基于各阶估计误差的联合过程估计权系数更新关系,格型联合估计器结构简单,QRD-LSL自适应滤波算法数值稳定性好。仿真结果表明本文提出的噪声控制方法有良好的噪声控制效果,收敛速度快,计算量小,稳态误差小,跟踪性能好。 相似文献