有效去除图像混合噪声的方法

脉冲耦合神经网络PCNN具有良好的脉冲传播特性,可以用于图像的边缘检测;多级中值滤波是对中值滤波的改进,具有较好的保护图像细节的作用;均值滤波是一种成熟的能较好滤除图像高斯噪声的方法.该算法充分结合上述3种算法的优点,先运用PCNN算法对被混合噪声干扰的图像进行边缘检测,然后利用多级中值滤波和均值滤波方法进行滤波处理.对不同的混合噪声进行滤除的实验结果表明本文算法较中值滤波和均值滤波方法有较大提升.     

一种改进的形态-小波阈值降噪方法

现场采集的旋转机械振动信号中一般存在强脉冲干扰和白噪声,小波阈值滤波对白噪声的滤波效果好,但对脉冲干扰的滤除效果不佳,而形态滤波虽然可以有效地剔除脉冲干扰,但不易滤除白噪声。针对这些问题,提出了一种基于形态滤波和改进的小波阈值滤波相结合的综合滤波方法。该滤波方法结合了两种滤波方法的优点,能够同时有效地滤除旋转机械振动信号中的脉冲干扰和白噪声。通过仿真信号和现场采集的转子振动信号进行了实验验证,结果表明,形态滤波与改进的小波阈值滤波相结合的滤波方法很好地滤除了转子振动信号中的噪声成分,进而提取出淹没在噪声中的转子振动信号。     

串脉冲超声导波断轨检测技术研究

针对传统的钢轨断轨检测效率低、无法进行实时检测的不足,对串脉冲超声导波断轨检测方法进行了研究.首先为了增强发射能量,同时达到脉冲叠加的目的,研发了脉冲个数、脉宽、间隔均可调的高压串脉冲超声导波发射系统;为了保证接收信号的信噪比和空间精度,用匹配滤波的方法对接收信号进行脉冲压缩.采用研制的串脉冲超声导波断轨检测系统,选用SH导波换能器在钢轨上进行了检测实验.结果表明,检测系统性能稳定,经过串脉冲叠加,使接收信号的幅值约为单脉冲接收信号的3倍,经过匹配滤波后,信噪比得到明显提升.该方法的换能器置于轨腰,能够对钢轨实现在线监测.     

用于图像处理的自适应中值滤波

针对标准中值滤波方法存在的不足,提出自适应中值滤波方法．该方法通过噪声检测确定子图像中的脉冲噪声点及噪声干扰大小,根据噪声干扰程度选择滤波窗口的尺寸,并采用改进的中值滤波方法对检测出的噪声点进行滤波．计算机模拟实验结果表明：自适应中值滤波能在有效地去除噪声的同时,较好地保护图像细节,较标准中值滤波具有更优良的滤波性能,其滤波速度也较中值滤波具有明显优势．     

采用脉冲宽度检测的自适应滤波算法

在对图像中干扰噪声模型分析的基础上,提出基于脉冲宽度和噪声模型检测的自适应噪声滤波算法,通过判定干扰噪声的脉冲宽度及分布密度来确定滤波器窗口的大小及类型,从而有效去除复合型干扰噪声.实验结果表明所提出的算法操作简单,对实际图像的处理效果优于一般常用噪声平滑滤波器的输出结果.     

一种新的基于噪声点检测的脉冲噪声去噪算法

中值滤波是广泛应用于去除脉冲噪声的一种非线性去噪方法,但是单一地使用中值滤波方法去除脉冲噪声会造成图像细节信息的丢失,从而使图像变得模糊。基于噪声点检测的脉冲噪声滤波方法可以在滤除噪声的同时有效地保持图像的细节信息。该文在此基础上提出了一种新的基于噪声点检测的脉冲噪声滤波算法,该算法在检测噪声点时用被检测点的中值滤波结果作为判定该点是否为噪声点的依据。而在滤除被检测到的噪声点时,采用的是迭代的中值滤波算法。从实验结果中可以看到,与其它中值滤波算法相比,该文的算法在去除脉冲噪声时能取得较好的效果。     

自适应中值滤波算法滤除医学图像脉冲噪声

实现了一种滤除医学图像脉冲噪声的自适应中值滤波算法,用均方根误差和噪声对原图像的毁坏程度两个客观评价指标对该算法及传统均值、中值滤波方法进行了比较与评价。根据设定条件检测滤波窗口中心像素是否为脉冲噪声,采取滤波窗口自适应的算法来滤除脉冲噪声,去除了传统中值滤波对所有像素均用中值代替的弊端,减少了不必要的图像细节损失。基于MATLAB的仿真实验表明,对于较大密度的脉冲噪声,该算法在有效抑制噪声的同时,能较好地保护边缘和细节信息。该算法已应用于虚拟内窥镜系统中,取得了令人满意的效果。     

激光扫描数据脉冲噪声自适应检测和滤除

针对现有脉冲噪声滤波算法在处理大斜率区域和断线型激光扫描数据中存在的多判和漏判问题,提出了一种有效检测脉冲噪声的方法.该方法依据局部脉冲噪声的分布,利用自适应弦偏差法进行噪声点检测;对噪声点自适应地选择滤波窗口中的非脉冲噪声数据进行中值滤波,以实现噪声滤除.实例结果表明：采用文中方法使得噪声检测更为准确,在滤除噪声的同时有效地保持了数据的细节.     

小波滤波在MR/GPS制导系统中的应用

针对MR/GPS制导火箭弹测量的地磁信号较弱、附加干扰噪声大的特点,运用小波分析的方法,设计了小波滤波器.提出对含脉冲干扰多的信号先采用中值滤波,再采用小波强制滤波的方法.仿真结果表明,该方法算法简单、易于实现.用小波变换处理后的地磁测量信号,与用FFT数字滤波器或数据平滑方法相比,不仅滤波效果更为理想,而且滤波后的信号能量损失很小.     

图像混合噪声的一种组合滤波消除方法

提出了一种基于高斯拉普拉斯边缘检测的含高斯噪声和脉冲噪声的图像组合滤波去噪方法,即首先对含有混合噪声的图像进行中值滤波,再用高斯拉普拉斯边缘检测方法检测出图像的边缘,得到边缘图像;然后利用自适应Wiener滤波对中值滤波后得到的图像进一步滤波去噪,最后将边缘图像嵌入经Wiener滤波得到的平滑图像中。此种方法不但能够有效去除含高斯噪声和脉冲噪声的图像中的噪声,而且可以保持图像的边缘信息,提高了图像的去噪效果和清晰度。     

Attribute augmentation-based label integration for crowdsourcing

Crowdsourcing provides an effective and low-cost way to collect labels from crowd workers. Due to the lack of professional knowledge, the quality of crowdsourced labels is relatively low. A common approach to addressing this issue is to collect multiple labels for each instance from different crowd workers and then a label integration method is used to infer its true label. However, to our knowledge, almost all existing label integration methods merely make use of the original attribute information and do not pay attention to the quality of the multiple noisy label set of each instance. To solve these issues, this paper proposes a novel three-stage label integration method called attribute augmentation-based label integration (AALI). In the first stage, we design an attribute augmentation method to enrich the original attribute space. In the second stage, we develop a filter to single out reliable instances with high-quality multiple noisy label sets. In the third stage, we use majority voting to initialize integrated labels of reliable instances and then use cross-validation to build multiple component classifiers on reliable instances to predict all instances. Experimental results on simulated and real-world crowdsourced datasets demonstrate that AALI outperforms all the other state-of-the-art competitors.     

从肌电信号中减少工频干扰方法的性能比较分析

如何从肌电信号中有效地减少工频干扰一直是肌电信号检测与应用中的突出问题。本文总结数字陷波、LMS自适应滤波、卡尔曼(Kalman)滤波和S变换等几种适合进行实时工频干扰去除的方法,研究和分析它们在去除肌电信号中工频干扰的性能。初步结果表明:Kalman滤波方法在从肌电信号中减少工频干扰方面表现出了较好的整体性能,而S变换方法对具有严重工频干扰的肌电信号具有较好的噪声抑制效果。     

A local stereo matching algorithm based on weighted guided image filtering for improving the generation of depth range images

Stereo matching is a challenging problem and highly accurate depth image is important in different applications. The main problem is to estimate the correspondence between two pixels in a stereo pair. To solve this problem, in the last decade, several cost aggregation methods aimed at improving the quality of stereo matching algorithms have been introduced. We propose a new cost aggregation method based on weighted guided image filtering (WGIF) for local stereo matching. The proposed algorithm solves multi-label problems in three steps. First, the cost volume is constructed using pixel-wise matching cost computation functions. Then, each slice of the cost volume is independently filtered using the WGIF, which substitutes for the smoothness term in the energy function. Finally, the disparity of any pixel is simply computed. The WGIF uses local weights based on a variance window of pixels in a guidance image for cost volume filtering. Experimental results using Middlebury stereo benchmark verify that the proposed method is effective due to a high quality cost volume filter.     

Investigation of pulse dispersion in a carrier‐based UWB system with LO leakage cancellation

Local oscillator (LO) leakage in a carrier‐based ultrawideband (UWB) system is a major design concern. In many cases, mixer LO‐RF isolation is not sufficient and the LO leakage is well above the useful UWB signal. However, this leakage can be substantially reduced by using a notch filter located before the UWB transmitting antenna as long as it will not lead to unacceptable signal distortion. Therefore, various filter parameters, such as the filter order and 3 dB rejection bandwidth, have been studied to see their effects on providing sufficient band rejection level to reduce the unwanted LO leakage while minimizing the transmitted pulse dispersion. Time domain simulations and measurements have been utilized to evaluate the pulse dispersion using both the relative signal's first pulse amplitude and the pulse time delay spread. © 2009 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.     

基于Frank编码的抗干扰方法

当干扰从雷达的主瓣方向进入时,采用自适应波束形成算法会导致主瓣波束变形、偏移等问题。针对干扰从雷达主瓣进入的问题,提出了一种对发射波形设计的抗干扰方法。当发射经Frank编码后的信号,利用Frank编码的正交性,构造出匹配滤波器和阻塞滤波器,分别将接收到的数据通过两滤波器,得到两路信号,然后利用自适应对消的方法将干扰抑制掉,从而达到抑制主瓣干扰的目的,通过仿真验证了利用波形设计的方法可有效抑制从主瓣进入的干扰。     

Superior foetal electrocardiogram signal elicitation using a novel artificial intelligent Bayesian methodology

Innumerable casualties due to intrauterine hypoxia are a major worry during prenatal phase besides advanced patient monitoring with latest science and technology. Hence, the analysis of foetal electrocardiogram (fECG) signals is very vital in order to evaluate the foetal heart status for timely recognition of cardiac abnormalities. Regrettably, the latest technology in the cutting edge field of biomedical signal processing does not seem to yield the desired quality of fECG signals required by physicians, which is the major cause for the pathetic condition. The focus of this work is to extort non-invasive fECG signal with highest possible quality with a motive to support physicians in utilizing the methodology for the latest intrapartum monitoring technique called STAN (ST analysis) for forecasting intrapartum foetal hypoxia. However, the critical quandary is that the non-invasive fECG signals recorded from the maternal abdomen are affected by several interferences like power line interference, baseline drift interference, electrode motion interference, muscle movement interference and the maternal electrocardiogram (mECG) being the dominant interference. A novel hybrid methodology called BANFIS (Bayesian adaptive neuro fuzzy inference system) is proposed. The BANFIS includes a Bayesian filter and an adaptive neuro fuzzy filter for mECG elimination and non-linear artefacts removal to yield high quality fECG signal. Kalman filtering frame work has been utilized to estimate the nonlinear transformed mECG component in the abdominal electrocardiogram (aECG). The adaptive neuro fuzzy filter is employed to discover the nonlinearity of the nonlinear transformed version of mECG and to align the estimated mECG signal with the maternal component in the aECG signal for annulment. The outcomes of the investigation by the proposed BANFIS system proved valuable for STAN system for efficient prediction of foetal hypoxia.     

Wavelet-based spectral shaping of UWB radio signal for multisystem coexistence

Although Ultra Wide Band (UWB) wireless systems supposed to coexist with other spectrally overlapping (narrowband) wireless systems, mutual interference degrades the performance of both systems. In this paper we investigate two techniques for reducing this mutual interference which do not require modification of the narrowband system. We propose to shape the spectrum of the Transmitted Reference (TR)-UWB signal using wavelet based multi-resolution technique to avoid its interference to other wireless systems. The reverse (narrowband) interference is alleviated using a notch filter at UWB receiver that could have much smaller bandwidth in this Gaussian pulse-shaped multi-resolution environment. TR simulations under AWGN and multipath channel conditions show that up to 55 dB reduction of UWB RF energy is possible at the interfering IEEE 802.11a band using the wavelet spectral shaping technique. The challenge is not to exceed the specified pulse duration while shaping the spectrum. The proposed technique does not reduce the throughput of the UWB system. It is general in the sense, it can be used for the better coexistence of any narrowband wireless system with UWB system.     

光纤网络中的云数据并行调度

光纤网络的通信质量受到云数据调度均衡性影响很大,为了改善光纤网络通信质量,提高网络中云数据传输的吞吐量和保真率,通过云数据并行调度,实现云数据传输均衡,提出一种基于自适应判决反馈均衡的光纤网络云数据并行调度模型;构建光纤网络通信的传输信道模型,采用最小均方误差估计方法进行光纤网络的量化融合估计,运用匹配滤波检测器进行云数据中的干扰滤波处理,结合自适应判决反馈均衡方法进行信道均衡,在均衡的信道中对滤波输出的云数据进行并行调度和多线程输出调制;仿真结果表明,采用该方法进行光纤网络中的云数据并行调度的均衡性较好,输出数据的保真率较高,误码率较低,改善了光纤网络的整体性能。     

基于动态概率抽样的标签噪声过滤方法

在机器学习问题中,数据质量对系统预测的准确性产生了深远的影响。由于信息获取的难度大,人类的认知主观且有限,导致了专家无法准确标记所有样本。而近年来出现的一些概率抽样方法无法避免样本人为划分不合理且主观性较强的问题。针对这一问题,提出一种基于动态概率抽样（DPS）的标签噪声过滤方法,充分考虑各个数据集样本间的差异性,通过统计各个区间内置信度分布频率,分析各个区间内置信度分布信息熵的走势,确定合理阈值。在UCI经典数据集中选取了14个数据集,将所提方法与随机森林（RF）、HARF、MVF、局部概率抽样（LPS）等方法进行了对比实验。实验结果表明,所提出的方法在标签噪声识别和分类泛化上均展示出了较高的能力。     

基于自适应时空码书的粒子滤波多目标跟踪

提出一种基于自适应时空码书检测模型的粒子滤波多目标跟踪算法。使用时空码书模型进行前景背景分割,检测出前景目标,在该模型上加入目标自适应过程。将自适应时空码书检测的结果作为粒子滤波跟踪算法的初始目标状态,通过关联算法和粒子滤波实现多目标跟踪。自适应时空码书模型能明显降低对前景目标的误检率,抑制噪声干扰。实验结果表明,该算法能够在有干扰的复杂背景下实现对运动多目标的快速捕获,并有效提高跟踪的可靠性和精度。