基于小波变换的零件图像数据融合

提出一种基于小波变换的图像数据融合的方法,对多源图像进行分解,将高频区域中的绝对值较大的系数作为重要小波系数;在低频区域,对逼近系数进行加权平均得到新的逼近系数,然后进行小波重构。实验表明,该方法是实用的。     

小波变换在AFM图像处理中的应用研究

根据原子力显微镜(AFM)图像的特点,对小波变换应用于原子力显微镜图像的降噪、增强及融合方法进行了阐 述,采用原子力显微镜对无磨料低温抛光后的铝合金(LY12)工件表面进行检测,并利用所述方法对扫描图像进行了处 理。结果表明,利用小波变换对原子力显微镜图像进行处理是有效的、可行的,得到满意效果。     

Enhancing nearfield acoustic holography using wavelet transform

When there are low signal to noise relationships or low coherences between measured pressure and a reference sensor, a pressure field measured and estimated by NAH (Nearfield Acoustic Holography) becomes noisy on the hologram and source planes. This paper proposes a method to obtain the high coherent de-noised pressure signals from low coherent noisy ones by combining a wavelet algorithm with NAH. The proposed method obtains the de-noised field from acoustic fields on a noise source plane reconstructed through backward propagation of NAH. Thus this method does not need high coherent pressure signals on the hologram surface while the conventional nearfield acoustic holography requires high-coherent signals. The proposed method was verified by numerical simulation using noisy signals, composed of original signals and imposed noises distributed on the hologram surface.     

基于小波变换和自适应中值滤波的图像去噪

小波图像去噪已经成为目前图像去噪的主要方法之一。该文尝试把小波变换与自适应中值滤波这两种去噪方法相结合,对同时含有高斯噪声和椒盐噪声的图像进行了去噪研究。实验结果表明,此方法在去除噪声的同时也较好地保留了原始图像的边缘信息,效果不仅优于单一的小波变换或普通中值滤波的方法,更优于将小波变换与普通中值滤波相结合的方法。     

基于小波域统计模型的纸浆纤维图像去噪研究

在小波多尺度分析基础上，提出一种新的图像小波系数的白适应统计算法，并应用于纸浆纤维图像的去噪研究。将图像小波系数视为服从广义高斯分布（GGD）的随机变量模型，在小波软阈值去噪的基础上引入空间自适应阈值方法；将均值滤波算法应用于小波系数方差的边缘估计中，结合最大后验概率准则（MAP）进行参数估计以恢复噪音小波图像。该算法用于纸浆纤维图像的去噪，效果理想，同其它的图像去噪算法相比，它具有较高的峰值信噪比（PSNR）。     

Linear filtering of 2-D wavelet coefficients for denoising ultrasound medical images

The paper presents a novel denoising method for ultrasound medical images, whose quality is degraded by the peculiar phenomenon of speckle noise. The method is constructed step-by-step on the basis of recent research on the topic, and consists in Gaussian filtering of proper wavelet coefficients of the image, corresponding to vertical and diagonal details. A comparison with other filtering techniques for ultrasound imaging, i.e. Wiener and median filter, is presented. The obtained results, combined with those reported in independent research, demonstrate that the proposed denoising scheme has very good performance and is very promising for actual medical application.     

Isolation and identification of dry bearing faults in induction machine using wavelet transform

Any vibration signal obtained from electromechanical systems contains a level of random changes. These random changes in the measured signal may be due to the random vibrations that can be related to the health of the machine for some faults such as dry bearing fault or bearing ageing. The presence of dry bearing fault, which is caused by the lack of lubricant, increases the level of random vibrations as compared to those obtained in healthy bearing machine. If these random vibrations could be isolated from the measured signal, useful information about bearing health may be obtained. Therefore, in this paper, signals (three line to line voltages, three currents, two vibration signals, four temperatures and one speed signal) obtained from the monitoring system are treated and analyzed using wavelet transform to correlate it to the dry bearing faults in induction machine. In this study, on-line analysis of the acquired signals has been performed using C⁺⁺, while MATLAB has been used to perform the off-line analysis.     

Tool wear monitoring in ramp cuts in end milling using the wavelet transform

Tool wear identification and estimation present a fundamental problem in machining. With tool wear there is an increase in cutting forces, which leads to a deterioration in process stability, part accuracy and surface finish. In this paper, cutting force trends and tool wear effects in ramp cut machining are observed experimentally as machining progresses. In ramp cuts, the depth of cut is continuously changing. Cutting forces are compared with cutting forces obtained from a progressively worn tool as a result of machining. A wavelet transform is used for signal processing and is found to be useful for observing the resultant cutting force trends. The root mean square (RMS) value of the wavelet transformed signal and linear regression are used for tool wear estimation. Tool wear is also estimated by measuring the resulting slot thickness on a coordinate measuring machine.     

基于小波能量谱的炮口脉冲噪声特性分析(英文)

火炮发射时产生的脉冲噪声对操作人员会造成不同程度的损伤或影响,必须进行人员防护,因此分析炮口脉冲噪声特性对噪声的治理和人员防护具有重要意义。针对傅立叶分析方法在脉冲噪声分析中的不足,提出了小波能量谱分析方法。通过对试验数据进行分析,得到了脉冲噪声的局域信号特征和频谱能量分布。实验结果表明,小波能量谱方法适用于炮口脉冲噪声特性分析。     

一种基于小波变换的分形图象编码方法

小波和分形相结合的图象编码方法,其基本思想是先利用小波变换实现图象的多分辨率分解,然后在小波域内采用分形理论对图象进行编码。本文提出了一种新的分形编码方法-基于固定区域定义域块的值域块编码。这种方法的优点是分形码不包含定义域块-值域块匹配对的坐标,并且值域块的尺寸小到2×2。图象仍能保证较好的压缩率和质量。仿真实验证明了该方法的有效性。     

Multi-scale wavelet transform filtering of non-uniform pavement surface image background for automated pavement distress identification

Non-uniform background of pavement images results in difficulties when segmenting pavement images for pavement distress identification. A novel and fast non-uniform background removal algorithm based on multi-scale wavelet transform is presented. The algorithm uses multi-scale wavelet transform to decompose pavement image and then reconstructs image background using low-wavenumber components through inverse wavelet transform. Brightness of image background is then corrected to achieve uniform background pavement image. The multi-scale wavelet transform algorithm is compared with median filter algorithm and morphological closing algorithm. Experimental results show that the proposed algorithm possesses the advantage of extracting tiny cracks more effectively than the other two algorithms, demonstrating its suitability to be used in automated pavement distress segmentation and identification.     

基于小波变换的零件图像融合与边缘检测

提出基于小波变换的零件图像数据融合和边缘检测的方法,对图像进行分解,将高频区域中的绝对值较大的系数作为重要小波系数;在低频区域,对逼近系数进行加权平均得到新的逼近系数,然后进行小波重构实现图像数据融合。应用小波变换对融合图像进行多尺度边缘检测,获取图像边缘,或对图像进行小波多尺度边缘检测,然后融合边缘。     

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.     

Identification of modal parameters using the wavelet transform

The wavelet transform is used as a time-frequency representation for the determination of modal parameters such as natural frequencies, damping ratios and mode shapes of a vibrating system. It is shown that using a particular form of the son wavelet function, results are improved compared to those obtained with the traditionally Morlet wavelet function. The accuracy of this new technique is confirmed by applying it to a numerical example and to ambient vibration measurements of a tower excited by wind.     

A method of noise removal using the inverse transform of an image autocorrelation function

A method for a reversible transform between an image and image autocorrelation function has been devised, and it has been applied to the reduction of random noise in a microscope image. Object-related features of an image signal as well as noise features are overlapped at the vicinity of the origin of an image autocorrelation, and a peak is formed at this position. The present method removes the noise contribution in this peak and returns the autocorrelation function, thus corrected, to the image. An autocorrelation function is computed by two Fourier transforms as based on the convolution theorem. The inverse transform from the image autocorrelation function is made possible by performing two Fourier transforms in reverse to the above. This method has been developed to remove random noise more accurately from a single image (especially for nonperiodic images) than other image-processing methods such as smoothing techniques and low pass filtering.     

用小波变换提高散斑能见度光谱测量法的灵敏度

在分析被动层颗粒温度含噪特点的基础上模拟了低信噪比的方波信号,根据变化规律,采用Mallat快速算法分析低信噪比的方波信号,并根据噪声分布特性设计了用于抑制被动层颗粒温度中干扰噪声的算法。对所设计算法进行仿真实验,结果表明,该算法可以最大限度地滤除信号中的噪声。通过搭建滚筒实验装置,测量滚筒被动层的颗粒温度,对测量数据进行分析,有效地测出了内部颗粒温度状态变化,表明了小波变换能有效提高测量被动层颗粒温度的信噪比。     

Application of the continuous wavelet transform in periodic error compensation

This paper introduces a new discrete time continuous wavelet transform (DTCWT)-based algorithm, which can be implemented in real time to quantify and compensate periodic error for constant and non-constant velocity motion in heterodyne displacement measuring interferometry. It identifies the periodic error by measuring the phase and amplitude information at different orders (the periodic error is modeled as a summation of pure sine signals), reconstructs the periodic error by combining the magnitudes for all orders, and compensates the periodic error by subtracting the reconstructed error from the displacement signal measured by the interferometer. The algorithm is validated by comparing the compensated results with a traditional frequency domain approach for constant velocity motion. The algorithm demonstrates successful reduction of the first order periodic error amplitude from 4 nm to 0.24 nm (a 94% decrease) and a reduction of the second order periodic error from 2.5 nm to 0.3 nm (an 88% decrease). The algorithm also reduces periodic errors for non-constant velocity motion overcoming limitations of existing methods.     

基于小波理论的电机电流信号消噪研究

小波分析是当前应用数学和工程学科中一个迅速发展的新领域。小波分析具有良好的多分辨分析能力,因而已广泛应用于信号和图像的消噪。而对电机电流信号进行消噪对于整个工程系统的分析具有很重要的意义,因此采用小波阈值消噪法对电机的电流信号进行了消噪。首先介绍了小波阈值消噪法的原理,然后用noisbump信号验证小波阈值消噪法的可行性,之后再用该方法对实验测取的电机电流信号进行消噪。消噪的效果表明:采用小波阈值消噪法对电机电流信号进行消噪,是一种可行的方法。     

数字超声波信号中有色噪声的自适应滤波

针对测试环境中常存在的超声波频段的有色干扰噪声,设计了一种基于横向滤波器和最小均方误差自适应滤波算法的自适应对消器结构,并提出了固定步长和自适应步长相结合的自适应滤波算法流程。该方法增设了一个接收环境噪声的专用探头来自动跟踪噪声特性的改变,无需手动设置自适应滤波器参数和期望信号。通过自适应步长调整算法与固定步长方法结合,该方法能够在实现良好滤波效果的同时兼顾快速跟踪环境的变化。实验表明,提出的方法可以有效滤除目标超声波信号频带之外频率点处的有色干扰噪声,信噪比改善幅度可达16dB;时间复杂度为O(n),可实现实时处理。本文方法可以在无人工干预下自动、实时、有效地滤除与超声波信号频率接近的有色干扰噪声,已被成功地应用于气体超声波流量测量中。