Image signal‐to‐noise ratio estimation using adaptive slope nearest‐neighbourhood model

A new technique based on nearest neighbourhood method is proposed. In this paper, considering the noise as Gaussian additive white noise, new technique single‐image‐based estimator is proposed. The performance of this new technique such as adaptive slope nearest neighbourhood is compared with three of the existing method which are original nearest neighbourhood (simple method), first‐order interpolation method and shape‐preserving piecewise cubic hermite autoregressive moving average. In a few cases involving images with different brightness and edges, this adaptive slope nearest neighbourhood is found to deliver an optimum solution for signal‐to‐noise ratio estimation problems. For different values of noise variance, the adaptive slope nearest neighbourhood has highest accuracy and less percentage estimation error. Being more robust with white noise, the new proposed technique estimator has efficiency that is significantly greater than those of the three methods.     

基于脉冲耦合神经网络模型的小波自适应斑点噪声滤除算法

分析了维纳滤波原理和脉冲耦合神经网络(PCNN)模型的特点,根据斑点噪声统计模型的特征,结合小波变换方法,提出了一种基于PCNN模型的小波自适应斑点噪声滤除算法(W-PCNN-WD)来改善超声图像质量.首先,对超声图像进行对数变换,使斑点噪声转换为加性噪声;对医学图像进行维纳滤波处理,计算其加性噪声的标准方差,并以此作为小波阈值.然后,利用小波变换对图像进行预处理,利用PCNN在小波域中对小波系数进行相应的修正.最后,进行小波逆变换和指数变换,获得滤除噪声的图像.结果表明:本文提出的滤波方法优于其他滤波方法,当噪声方差为0.01时,本文滤波算法获得的峰值信噪比(PSNR)比经Wiener滤波方法获得的高出9 dB.该滤波方法能在有效去除超声斑点噪声的基础上保留图像的边缘细节信息,极大地改善了图像的视觉质量.     

基于MAP估计的复小波域局部自适应绝缘子红外热像去噪方法

为了从强白噪声干扰的红外热像中提取真实的绝缘子盘面温度场信息,提出一种基于MAP估计的复小波域局部自适应去噪方法.首次证实了绝缘子红外热像双树复小波变换(DT-CWT)系数服从拉普拉斯分布,并对不同滤波器组采用各自最精细分解层子带系数估计噪声方差,利用待估计点圆形邻域系数估计信号方差,且随分辨率变化调整圆形邻域半径,使得MAP估计的无噪声系数更为准确,提高了去噪图像质量.实验结果表明,该方法比传统的Wiener滤波法、基于离散小波变换和DT-CWT的贝叶斯阈值去噪方法具有更高的信噪比,在有效去除图像噪声的同时,图像细节信息保留更完好.     

Image noise reduction using Wiener filtering with pseudo-inverse

We present a preliminary design and experimental results of a Gaussian noise reduction method for ultrasound images. Our method utilizes a Wiener filtering algorithm with pseudo-inverse technique. The method is capable of solving the Gaussian noise problem in ultrasound image by setup a constant dB of noise function. The key idea of the Wiener filtering algorithm is to process the given ultrasound signal by making the filtering less sensitive to slight changes in input conditions. In this paper, we investigate the possibility of employing this approach for pre-processing ultrasound image application. The application of the proposed method for reducing Gaussian noise is demonstrated by four examples. Meanwhile, we also made the comparisons with median filter, mean filter and adaptive filter; the results reveal that the proposed method has the best noise filtering capability than other three methods. The results also show that the proposed method produces recovery images with quiet high peak-signal-to-noise ratio.     

Signal‐to‐noise ratio enhancement on SEM images using a cubic spline interpolation with Savitzky–Golay filters and weighted least squares error

A new technique based on cubic spline interpolation with Savitzky–Golay smoothing using weighted least squares error filter is enhanced for scanning electron microscope (SEM) images. A diversity of sample images is captured and the performance is found to be better when compared with the moving average and the standard median filters, with respect to eliminating noise. This technique can be implemented efficiently on real‐time SEM images, with all mandatory data for processing obtained from a single image. Noise in images, and particularly in SEM images, are undesirable. A new noise reduction technique, based on cubic spline interpolation with Savitzky–Golay and weighted least squares error method, is developed. We apply the combined technique to single image signal‐to‐noise ratio estimation and noise reduction for SEM imaging system. This autocorrelation‐based technique requires image details to be correlated over a few pixels, whereas the noise is assumed to be uncorrelated from pixel to pixel. The noise component is derived from the difference between the image autocorrelation at zero offset, and the estimation of the corresponding original autocorrelation. In the few test cases involving different images, the efficiency of the developed noise reduction filter is proved to be significantly better than those obtained from the other methods. Noise can be reduced efficiently with appropriate choice of scan rate from real‐time SEM images, without generating corruption or increasing scanning time.     

Gear fault diagnosis using active noise cancellation and adaptive wavelet transform

Acoustic signal from a gear mesh with faulty gears is in general non-stationary and noisy in nature. Present work demonstrates improvement of Signal to Noise Ratio (SNR) by using an active noise cancellation (ANC) method for removing the noise. The active noise cancellation technique is designed with the help of a Finite Impulse Response (FIR) based Least Mean Square (LMS) adaptive filter. The acoustic signal from the healthy gear mesh has been used as the reference signal in the adaptive filter. Inadequacy of the continuous wavelet transform to provide good time–frequency information to identify and localize the defect has been removed by processing the denoised signal using an adaptive wavelet technique. The adaptive wavelet is designed from the signal pattern and used as mother wavelet in the continuous wavelet transform (CWT). The CWT coefficients so generated are compared with the standard wavelet based scalograms and are shown to be apposite in analyzing the acoustic signal. A synthetic signal is simulated to conceptualize and evaluate the effectiveness of the proposed method. Synthetic signal analysis also offers vital clues about the suitability of the ANC as a denoising tool, where the error signal is the denoised signal. The experimental validation of the proposed method is presented using a customized gear drive test setup by introducing gears with seeded defects in one or more of their teeth. Measurement of the angles between two or more damaged teeth with a high level of accuracy is shown to be possible using the proposed algorithm. Experiments reveal that acoustic signal analysis can be used as a suitable contactless alternative for precise gear defect identification and gear health monitoring.     

基于小波域维纳滤波的光纤面板暗影检测

通过分析光纤面板透光图像的噪声性质,提出在小波去噪的始末对图像取对数、指数变换,成功转换噪声模型,有效去除高斯及斑点噪声.并提出在小波域采用维纳滤波算法以增强去噪功能,实现更为有效地去除光纤面板透光图像中的噪声.最后对去噪图像进行暗影检测,实验结果表明,采用此算法进行去噪,检测出的暗影定位更加精准,冗余信息大量减少,有...     

Signal‐to‐noise ratio estimation on SEM images using cubic spline interpolation with Savitzky–Golay smoothing

A new technique based on cubic spline interpolation with Savitzky–Golay noise reduction filtering is designed to estimate signal‐to‐noise ratio of scanning electron microscopy (SEM) images. This approach is found to present better result when compared with two existing techniques: nearest neighbourhood and first‐order interpolation. When applied to evaluate the quality of SEM images, noise can be eliminated efficiently with optimal choice of scan rate from real‐time SEM images, without generating corruption or increasing scanning time.     

基于dbN小波变换和自适应高斯滤波的齿面干涉图像相位滤波方法

齿轮齿面形貌的激光干涉测量中，由于齿面高度差较大，采集到的干涉图像中难以避免存在条纹密集区域，容易出现局部条纹粘连、错切等现象，增加了相位噪声和解包裹难度。分析了包裹相位图中条纹密度分布规律，提出了一种基于dbN小波变换和自适应高斯滤波的齿面干涉图像相位去噪方法。首先，利用小波变换分解出包裹相位图中常表现为高频信号的噪声，采用软阈值去噪滤除部分高频噪声；其次，根据包裹相位图频域特征，结合自适应高斯滤波进一步对高频噪声进行迭代滤波处理；最后，设计了相关实验，通过与经典的滤波方法进行对比，所提方法不仅能够有效滤除条纹较为密集的包裹相位图中的相位噪声，而且更大限度地保留了图像细节信息，证明了所提方法的有效性和正确性。     

小波变换的流体压力信号自适应滤波方法研究

为了有效地消除流体压力信号中的噪声,提出了一种基于小波变换的自适应滤波算法,该算法针对信号和噪声经小波变换后在不同尺度上的特征不同,先对信号进行小波多尺度分解,然后对各尺度分解的信号分别选用不同的滤波参数,进行自适应滤波处理,并用该方法对液压系统运行中采集的压力信号进行降噪处理.试验结果表明,该方法比普通的自适应滤波方法能更有效地消除流体压力信号中的噪声.     

基于自适应最优Morlet小波的滚动轴承故障诊断

滚动轴承早期故障信号中故障信息比较微弱常常被强噪声所掩盖,增加了对滚动轴承故障诊断的难度。针对这一问题,笔者提出了基于自适应最优Morlet小波变换的滚动轴承故障诊断方法。首先,利用粒子群优化算法对Morlet小波变换的核心参数进行自适应寻优,在获得最优Morlet小波的同时保证了良好的带通滤波性能;然后,将最优Morlet小波对滚动轴承早期故障信号进行滤波去噪,提高信号的信噪比;最后,对最优Morlet小波滤波信号进行包络谱分析,通过包络谱中的主导频率成分与滚动轴承各元件的故障特征频率对比从而判断轴承的故障位置。仿真数据和实测数据分析结果证明,笔者所提方法能够有效提取故障信号中的特征信息,具有一定的有效性。     

基于小波包分解与主流形识别的非线性降噪

为解决工程实际中强噪声、非线性且频率成分复杂的振动信号降噪问题,提出了基于小波包分解和主流形识别的非线性降噪方法。采用小波包分解将原始振动信号正交无遗漏地分解到各频带范围内,根据各子频带中信噪空间分布,分别采用相应参数对小波包分解系数进行相空间重构;采用局部切空间排列(local tangent space alignment,LTSA)主流形识别方法在高维相空间中实现信号与噪音的分离,并重构出降噪后的一维小波包分解系数,最后进行小波包分解重构得到降噪后的振动信号。通过仿真实验和实例应用对本文所提方法的有效性进行了验证,试验结果表明本文方法具有良好的非线性降噪能力。     

非平稳信号自适应滤波的小波模型与滤波方法

分析自适应滤波和小波滤波的原理与方法,建立非平稳信号的自适应滤波的小波模型和滤波方法。利用小波变换的多尺度分解,将分离出来的噪声成分作为自适应滤波器的输入信号。通过自适应滤波器组能同时实现对多种噪声成分的最佳滤波,是实现信噪分离的最佳滤波方法,具有优良的滤波性能。模型验证和工程实例应用表明,该方法能实现非平稳信号在同频段对噪声成分和有用信号的最佳估计。     

一种具有边缘保持特性的超声图像小波域阈值去噪新方法

超声图像去噪是医学图像处理的研究热点之一,基于小波域阈值去噪技术及阈值选取方法的分析,提出一种新的医学超声图像小波域阈值去噪方法.这种方法采用半-软阈值去噪技术和广义交叉确认函数寻找阈值,在有效去噪的同时较好地保留了图像边缘细节.首先, 把对数超声图像小波分解;然后,基于广义交叉确认函数寻找最小均方误差意义上的近似最优阈值,对所有的高频段采用半-软阈值去噪; 最后, 经小波反变换和指数变换获得去噪后的超声图像,文末对超声图像小波域阈值去噪方法作出定性比较,并对算法的去噪性能给出定量分析.仿真实验和实际测试结果表明此方法是有效的、可行的.     

Optimal and near-optimal filters in high-resolution electron microscopy

Two filters for improving the visibility of crystalline material in the presence of amorphous surface contamination layers in high-resolution electron microscope images can be constructed automatically from the information present in the Fourier transform of the recorded image. The recorded signal is considered in the first approximation to be the sum of two signals which are uncorrelated in the frequency domain. By estimating the power spectrum of the signal from the amorphous layer, an optimized estimate for the desired signal is given by the Wiener filter. A second filter which uses the estimated amplitude of the amorphous signal to subtract out a background can be shown to be related to the Wiener filter. The two filters are applied to an experimental image of zeolite and the effects of the two filters are compared.     

SF6断路器气体泄漏红外图像中散斑噪声的抑制算法

SF6断路器是电力系统中常见的电气设备,针对SF6断路器气体泄漏红外图像中广泛存在的激光散斑噪声,提出一种抑制散斑噪声的新算法.将同态均值滤波与小波变换结合,并应用贝叶斯软阈值,对红外图像进行去噪处理.研究中,将该算法与同态均值滤波、同态均值滤波与传统周定小波阈值结合滤波等算法作了比较,结果表明,在图像标准偏差、信噪比和散斑指数等常用性能指标上所提算法优于其他算法.     

DT-CWT相关滤波在齿轮箱故障诊断中的应用

根据小波系数的相关分析理论,提出了基于双树复小波变换的小波相关滤波法。该方法根据相邻层小波系数的相关性,通过迭代过程自适应地进行滤波,能够在达到良好降噪效果的同时保留微弱故障特征信息。对降噪后的信号进行希尔伯特包络分析便可准确得到故障特征频率。试验信号分析与工程应用结果表明,该方法能够有效提取强背景噪声下的齿轮箱轴承早期故障特征信息。     

基于DT-CWT自适应Teager能量谱的轴承早期故障诊断

针对滚动轴承早期故障特征信息难以识别以及带通滤波器参数设置依赖使用者经验等造成共振带不能有效确定并自适应提取的问题,提出了频带幅值熵的概念。在此基础上,将双树复小波变换和Teager能量谱结合,提出了基于双树复小波自适应Teager能量谱的早期故障诊断方法。首先,利用双树复小波将采集到的振动信号分解为不同频带的子信号,并计算各子带的频带幅值熵;然后,将熵值按升序排列后依次作为阈值,提取频带幅值熵大于阈值的子带,依据峭度指标确定最佳阈值,从而自适应并且有效地提取出共振带;最后,对共振带进行Teager能量谱分析,即可从中准确地识别出轴承的故障特征频率。通过信号仿真与实验数据分析验证了该方法的有效性。     

A new denoising algorithm for MFL data obtained from seamless pipeline inspection

The magnetic flux leakage (MFL) nondestructive evaluation technique is used extensively for in-service inspection of gas and oil pipelines. Unfortunately, the MFL data obtained from seamless pipeline inspection is usually contaminated by various sources of noise, which considerably reduces the detectability of defect signals in MFL data. In this paper, a new denoising algorithm is presented for removing seamless pipe noise (SPN) and system noise contained in MFL data. The algorithm first utilizes the new wavelet domain adaptive filtering method proposed by combining wavelet transform with the adaptive filtering technique to remove SPN contained in MFL data and then exploits the coefficient denoising approach with wavelet transform to cancel the system noise in the output of the wavelet domain adaptive SPN cancellation system. Theoretical analysis shows that the proposed denoising algorithm has a better overall performance than the existing denoising algorithm. Results of application of the proposed algorithm to MFL data from field tests are presented to demonstrate the performance of the proposed algorithm compared with the existing denoising algorithm. The text was submitted by the author in English.     

小波神经网络在CCD图像噪声处理中的应用

目的：CCD相机响应功能的非线性,导致了CCD噪声模型的复杂性,使得滤波效果不佳,本文提出一种针对于数字图像中CCD噪声的小波神经网络滤波器。方法：首先,分析CCD噪声模型,找出导致CCD噪声模型复杂的原因——CCD相机响应功能（camera response function简称CRF）的非线性;接着,在对ANS滤波器分析的基础上,针对影响滤波效果的两大问题：滤波窗口和图像强度,将小波神经网络非线性逼近CCD噪声曲线,按照噪声参数对图像进行区域划分并分配相应的权值,然后结合相应的非线性滤波器进行针对性滤波,最后综合输出。结果：实验结果表明：本文改进的滤波器滤波效果明显,信噪比得到进一步提高（24.65）。结论：利用神经网络良好的非线性函数逼近性,将其结合ANS滤波器构造出神经网络非线性ANS滤波器（NN-NANS filter）,试验结果表明,该滤波器在去除噪声的同时边缘细节也得到了很好的保留,同时提高了信噪比。