Demodulation of a single interferogram based on continuous wavelet transform and phase derivative

A novel method based on continuous wavelet transform (CWT) and guidance of phase derivative is developed to measure the phase of a single fringe pattern which contains closed fringes. Wrapped phase values are retrieved by ridge extraction algorithms based on CWT which has the capability of better noise reduction and thus increases the resolution of measurement significantly. To further reduce the noise, the scales detected by maximum ridge algorithm are filtered iteratively before retrieval of wrapped phase. The proposed method also identifies any ambiguous point in a non-monotonous fringe pattern by directly tracking an inflexion point from an unwrapped phase map without the use of a carrier. The algorithm developed is validated by computer simulation and experimental results. Based on micro interferometry the experimental results for both static and dynamic deformations of a micro structure demonstrate that the proposed method is an effective tool for the analysis of closed fringe patterns and subsequent deformation measurement. However, the proposed technique is limited to measurement of surface which is relatively smooth compared to the mean wavelength of the light source. In addition, prior knowledge of the sign of surface slope is required in cases where a spatial carrier is not available or adaptable.     

Modal identification based on Gaussian continuous time autoregressive moving average model

A new time-domain modal identification method of the linear time-invariant system driven by the non-stationary Gaussian random force is presented in this paper. The proposed technique is based on the multivariate continuous time autoregressive moving average (CARMA) model. This method can identify physical parameters of a system from the response-only data. To do this, we first transform the structural dynamic equation into the CARMA model, and subsequently rewrite it in the state-space form. Second, we present the exact maximum likelihood estimators of parameters of the continuous time autoregressive (CAR) model by virtue of the Girsanov theorem, under the assumption that the uniformly modulated function is approximately equal to a constant matrix over a very short period of time. Then, based on the relation between the CAR model and the CARMA model, we present the exact maximum likelihood estimators of parameters of the CARMA model. Finally, the modal parameters are identified by the eigenvalue analysis method. Numerical results show that the method we introduced here not only has high precision and robustness, but also has very high computing efficiency. Therefore, it is suitable for real-time modal identification.     

Gabor transform and continuous wavelet transform for model pulsed signals

A model of a pulsed signal in the form of a superposition of elementary nonstationary signals is considered. The parameters of such a superposition are chosen so that signal amplitude A(t) strongly varies with time. For such a signal, the analytic expression for the Gabor transform (GT) and continuous wavelet transform (CWT) using the mother Morlet wavelet are analyzed. A criterion is proposed for the matched behaviors of GT and CWT with signal amplitude A(t). The advantages of the CWT adaptively selecting the window size over the GT whose explicit form depends on the window size are demonstrated. The proposed method can be used for analysis of many transient stages of time-dependent signals in various branches of physics.     

一种基于小波变换的跨带矢量量化

由小波变换产生的子带之间存在着较强的相关性,然而目前的在基于小波变换的图像编码矢量量化中,码矢往往仅由带内系数组成,没有充分利用带间相关性。本文提出一种新的跨带矢量量化算法,码矢由在同级子带中的相应系数组成。在三次小波变换下,第一级子带的码矢维数为４８Ｄ,第二级为１２Ｄ,第三级为３Ｄ。这样形成的码矢既匹配了各级子带的重要性,又利用到了带内和带间相关性,在相同的比特率下,其重构图像的信噪比ＰＳＮＲ比码矢仅由带内系数组成的传统带内ＶＱ高出１～２ｄＢ。     

基于小波高频的立体图像质量评价

根据小波变换的高频分量包含边缘及纹理等人眼比较关注的细节信息,提出了一种基于小波变换的高频重构的立体图像质量评价方法,包括左右视点质量评价和立体感评价两部分。通过模拟人类视觉系统处理原始和失真的左右视点图像,利用结构相似度算法得到左右视点质量评价值;用加权处理后的小波高频分量经过重构得到人眼关注的细节信息图,分别计算原始和失真的左右视点细节信息图的绝对差值图,利用结构相似度算法对两幅绝对差值图评价得到立体感质量评价值。将两种质量评价值进行加权融合得到最终的立体图像质量评价值。整体实验结果的Pearson线性相关系数在0.94以上,Spearman秩相关系数值在0.93以上,均方根误差值接近5.5,能较好地符合人眼视觉特性。     

基于小波变换的蓝宝石荧光光纤温度计

采用激光加热小基座法生长出掺Cr3+的蓝宝石光纤荧光温度传感头，它具有结构紧凑，耐高温等特点，测温范围从室温到450℃。使用基于小波变换的数据处理方法，有效去除信号中的噪声，提高了信噪比。在对荧光测温机理和有关光纤技术进行分析的基础上，采用与调制荧光信号相关的双参考源相位锁定测量方案，可在无激励光干扰的情况下对荧光寿命进行实时测量。根据噪音和信号在小波变换下表现出的不同性质，提出以小波变换为基础的温度信号特征提取及消噪方法。与其它处理方法相比，小波变换方法可以克服傅里叶变换对突变信号不起作用的缺点，同时又比Gabor变换具有可变窗口的优点。该方法可以缩短测量时间，提高测量分辨率。     

基于光学子波变换的SDF匹配滤波器

提出了一种基于光学子波变换的综合判别函数匹配滤波器(WFSDF),将光学子波变换和综合判别函数相结合,只用一个简单的4f系统就能实现输入图像和SDF子波变换的相关运算。采用计算全息的方法制作复数匹配滤波器。计算机模拟仿真结果表明,与传统的SDF匹配滤波器相比,WFSDF匹配滤波器,锐化了相关峰,提高了识别率,同时简化了光路,能够实现多目标的畸不变识别。     

基于离散平稳小波变换的红外图像去噪

提出了一种基于离散平稳小波变换的红外图像去噪方法。在预先不知道噪声方差的前提下,只利用红外图像的输入数据就可以确定所要求的渐近最优阈值。对红外图像进行离散平稳小波变换后,分别对各个分解层的高频子带利用所提出的方法进行迭代去噪,使各个高频子带分别收敛于其最大信噪比。实验结果表明,所提出的方法在有效的去除红外图像噪声的同时,又能较好的保持红外图像的细节部分信息。算法在性能指标和视觉质量上均优于基于离散正交小波变换的阈值去噪方法和传统的中值滤波法。     

基于小波变换的信号去噪在瑞利-泰勒不稳定性定标实验中的应用

 采用Daubechies小波分析,对神光Ⅱ装置上流体力学不稳定性实验中样品扰动幅度和X光强度之间定标关系的数据进行了处理,获得了比较理想的去噪效果,并由此得到了平面调制靶波长及材料线性吸收系数等细节信息。该定标关系对准确测量瑞利-泰勒不稳定性的增长至关重要。作为对比,利用Wiener滤波方法对数据进行了处理,结果显示在处理这类信号时,Daubechies小波滤波在去除噪声和保留信号细节特征方面明显优于Wiener滤波。     

Phase recovery from fringe patterns using the continuous wavelet transform

Interferometry is well established as an optical technique in which a measurand is encoded as the phase of a periodically varying intensity pattern. In view of the inherent accuracy of interferometry, many methods have been developed to retrieve the phase from images of the fringe pattern. Our focus in this paper is one such technique—the continuous wavelet transform (CWT). We begin by reviewing the CWT and the space–spatial–frequency localisation properties of wavelets. We show that a path which follows the maximum modulus of the CWT (the wavelet ridge) gives the instantaneous fringe frequency as a function of spatial displacement. The phase is automatically and trivially obtained, without discontinuities, by integration. Examples of practical wavelets are given and algorithms to isolate the wavelet ridge reviewed.     

小波变换在变压器励磁涌流识别中的应用

为防止励磁涌流导致变压器的差动保护误动作,本文提出了一种以小波系数分布为判据的励磁涌流识别的新方法。该方法以小波变换中多分辨分析为理论依据,通过小波变换后对所得二尺度上的小波系数进行提取,计算其方差变化率来表示小波系数的变化情况,并设定合理的阀值,来对励磁涌流与故障电流进行区分。应用MATLAB工具箱搭建变压器励磁涌流与故障电流仿真模型,进行大量仿真,仿真试验结果表明,该方法能够快速有效的对励磁涌流与故障电流进行识别判断,克服了传统方法的不足,保证差动保护的正确动作,具有很高的应用前景。     

基于小波变换的共焦拉曼图像去噪方法

针对现有共焦拉曼光谱显微图像噪声去除不足的问题,提出一种基于小波变换的拉曼图像全面去噪方法。首先对拉曼光谱图像进行小波变换处理,然后再对拉曼图像进行双边滤波处理,进而实现拉曼光谱显微图像中噪声干扰的抑制。理论分析和实验表明:与现有拉曼光谱显微图像去噪方法相比,所提方法使拉曼光谱显微图像的峰值信噪比和结构相似度分别改善了92.99%和197%,其有效提高了拉曼光谱图像的信噪比和物质成分分析的准确率。     

基于小波变换的脑部医学Demons图像配准

非刚性配准是医学图像处理的一个重要研究方向。针对Demons衍生出的一系列经典的配准算法在医学图像应用上计算复杂、方向信息不足问题进行了研究。基于光流场模型的Demons算法依赖图像灰度梯度是图像发生变形,当缺乏梯度信息时,力不能确定,因而容易造成误差,并且该算法仅适合于单模态图像配准。为此本文提出了一种基于小波变换理论的频域Demons配准处理方法(B-Demons)。该方法利用小波变换能够对各个尺度、方向和位置实现较好定位的优势,通过高频、低频的图像变换反映出图像的特征信息。实验结果证明了算法的有效性和鲁棒性。     

Target recognition in clutter scene based on wavelet transform

Edge extraction based on wavelet for optical correlation detection is presented. Optical experiments with joint transform correlator (JTC) show that there is a bright application prospect in the field of optical correlation detection by extracting the edge features of input image with the method of wavelet transform. In the course of processing, the multi-scale character of wavelet is used sufficiently. The energy of correlation peaks and the detection ratio of various targets are greatly enhanced by the approach. To demonstrate the feasibility of edges extraction based on WT, small targets and targets in clutter scene are successfully detected.     

Eliminating the zero spectrum in Fourier transform profilometry using a two-dimensional continuous wavelet transform

In this paper, a filtering technique based upon two-dimensional continuous wavelet transform (2D-CWT) is used to eliminate the low frequency components of fringe patterns. The filtered fringe patterns are subsequently demodulated using a standard Fourier transform profilometry (FTP) algorithm. This image pre-filtering stage improves the noise performance of the FTP algorithm and enables the FTP method to demodulate fringe patterns with larger bandwidths. Also, the 2D-CWT technique reduces speckle noise significantly. Moreover, only a single fringe pattern is required in this technique. The 2D-CWT algorithm is capable of separating low frequency terms from the high frequency terms that contain phase-modulated fringe information, even when both interfere, greatly, in the frequency domain. The proposed algorithm is tested, both via computer simulation and using real fringe patterns. This revealed the robustness of this algorithm and also demonstrably enables the demodulation of a wider range of fringe patterns using the FTP technique.     

Audio watermarking based on psychoacoustic model and critical band wavelet transform

Watermark embedding algorithm based on critical band wavelet transform of digital audio signal is proposed in this paper. The masking threshold for each audio signal segment was calculated on the basic of psychoacoustic model. According to the similarity between critical band of human auditory system and critical band wavelet transform, a watermark was embedded into the low-band and mid-band coefficients of digital wavelet. The embedding strength was adaptively controlled by the masking threshold. The experiment results show that the embedded watermark signal is inaudible, and the watermarked audio signal has good robustness against many attacks such as compression, noise, re-sampling, low-pass filtering.     

Hybrid two-dimensional continuous wavelet transform for analysis of phase-shifted interferograms

In interferogram or fringe pattern analysis, a technique capable of automatically analyzing interferograms that contain complex fringes and high-level noise as well as defects is highly demanded. In this paper, a hybrid technique combining the concepts of the two-dimensional continuous wavelet transform technique and the phase-shifting technique is proposed. The novel technique takes the advantages of the two existing techniques, and has the ability to accurately and automatically analyze multiple phase-shifted complex interferograms involving noise and defects. The validity of the technique is demonstrated by both computer simulation and real experiment.     

Super-resolution image restoration algorithm based on orthogonal discrete wavelet transform

By using orthogonal discrete wavelet transform(ODWT)and generalized cross validation(GCV),and combining with Luck-Richardson algorithm based on Poisson-Markovmodel (MPML),several new superresolution image restoration algorithms are proposed.According to simulation experiments for practical images,all the proposed algor ithms could retain image details better than MPML,and be more suitable to low signal-to-noise ratio(SNR)images.The single operation wavelet MPML(SW-MPML)algorithm and MPML algorithm based on single operation wavelet transform(MPML-SW)avoid the iterative operation of self-adaptive parameter in MPML particularly,and improve operating speed and precision.They are instantaneous to super-resolution image restoration process and have extensive application foreground.     

基于小波变换与小域特征模糊融合的人脸识别

小波变换是一种很好的图像压缩方法,利用小波变换对人脸图像进行三次小波分解,并将低频分量分割成为7个子图像。鉴于人脸上的各小域子图像信息的相互独立性。先利用小域子图像实现软分类,然后使用传统奇异值分解(SVD)法提取出各小域子图像的奇异值(SV),构造出小域奇异值特征向量,给出待识别图像对训练样本图像的隶属度,并采用模糊融合的方法对小域特征进行数据融合,获得识别结果。实验结果表明,该方法实现起来简单、识别速度快,具有很高的识别率。