基于主元分析的随机相移算法

将统计理论中的主元分析应用到干涉条纹分析中,提出一种基于主元分析的随机相移算法。该算法先将多帧条纹数据分解成不相关的主元成分,然后从主元成分中提取2维相位信息。数值模拟结果表明:该算法不需要迭代运算就能从4帧完全随机相移干涉图中有效提取相位信息,运算时间少,精度高。实验结果表明主元分析相移算法比现有迭代相移算法更适合于随机相移干涉图分析。

     

基于主元分析的干涉仪相移器校准方法

利用主元分析从一组干涉图中提取相位和相移量,得到相移量与驱动电压之间的对应关系,从而实现压电陶瓷相移器的非线性校准.模拟结果表明:主元分析法计算相移量的误差随着干涉图中条纹根数的增多而减小;当干涉图中条纹根数多于0.25时,相移量计算误差小于0.1664rad.实验结果表明:相移量计算误差是周期分布,对相移器校准准确度影响小.该方法无需迭代计算,且对干涉图中条纹根数没有严格要求,因此是一种高效且实用的校准方法.     

基于迭代优化的两帧随机相移干涉术

提出了一种由粗及精的无需预滤波的两帧先进迭代随机相移技术。基于最小化残差背景,首先利用采集条纹图的相关运算,在近似正交条件下估计相移和相位;然后利用这些求解过程中的中间参数在空域由条纹图数据估计相移和背景;最后,迭代上述过程直至收敛到预定义的精度,从而实现相位的精确求解。针对条纹图空域背景和振幅变化问题,利用条纹图子图降低背景空域变化对背景和相移估计的影响,并减小运算量。与现有的方法相比,本文方法由于提供了正确的初值,具有收敛快、计算快的特点。本文方法简单可靠,是一种实用的两帧相移干涉方法。     

基于快速最小二乘法的两步随机相移算法研究

在相移干涉测量中,为了在较短时间内实现较高的精度,提出了一系列基于快速最小二乘法的两步随机相移算法.以双滤波和差归一化算法、单滤波和差归一化算法和格兰-施密特正交化算法计算出来的相位作为迭代初始值,利用没有滤波的两幅相移干涉图进行最小二乘法运算以获取最终的相位,为了节省时间,只选取有限数量的像素来参与迭代运算.通过比较...     

基于自调谐相移干涉算法的相移误差分析

相移干涉技术由于其测量精度高的特点被广泛应用于波面检测干涉仪中。相移误差为测量过程中主要误差来源。基于一种自调谐相移干涉算法,研究在标定误差和随机相移误差下,算法的波前相位还原精度。对于标定误差,算法能精确地求解出实际相移步长,从而极大地提高了相位还原精度。与经典五步Hariharan算法对比,仿真结果表明,该算法的相位还原PV(峰谷)、RMS(均方根)误差响应更低,其PV误差响应远低于10^-3λ(λ为光源中心波长),而Hariharan算法在10^-3λ量级。基于自调谐算法在标定误差时的相位求解过程,扩展该算法以更适用于随机相移误差。在相同20%随机相移误差范围内,与Hariharan算法计算结果偏差的绝对值接近10^-9λ,能达到较高还原精度。将该自调谐算法运用在干涉仪测量光学元件表面形貌实验中,实验结果表明,与Hariharan算法相比,自调谐算法在仅存在标定误差时,能较明显地抑制纹波误差,两者计算面形PV存在偏差。在较小振动环境下,两种算法计算得到的相位面形分布高度一致。     

基于最小二乘迭代的多光束相移算法

高阶谐波和随机相移误差是影响干涉测量精度的主要因素.为了同时解决这两问题,提出了基于最小二乘迭代的多光束干涉条纹分析方法.该方法利用傅里叶级数将多光束干涉条纹展开为基波和各阶谐波之和.它只需要5帧随机相移的多光束干涉条纹,即可通过最小二乘迭代准确地求得相移值和相位分布.模拟计算结果表明,当测试面反射系数小于0.6、随机相移误差的均方根小于1时,只需10次迭代运算即可将误差控制在0.005 (PV)和0.003(RMS)rad之下,精度比传统的五步算法精度高.实验结果进一步验证了该算法的有效性,并表明该算法比双光束相移算法优越.     

相移技术中五步等步长Stoilov算法的性能分析

Stoilov算法是近几年提出的一种五步等步长相移算法。有关文献中的误差分析表明 ,该算法的性能优于四步等步长Carr啨算法闹懈隽耍樱簦铮椋欤铮鏊惴ǖ恼繁泶锸?,采用线性误差理论详细分析了算法的性能 ,尤其是算法性能对相移步长的依赖关系。分析表明 ,可以选择一个最佳的相移步长以有效减少位相测量误差 :相移步长为 5 2°时可有效抑制二次相移量误差的影响 ;相移步长为 90°时可极大地减少光强误差的影响。最后给出了Stoilov算法与Carr啨算法和Hariharan算法的比较。     

一种变频相移干涉测量的相位提取算法

相位提取的精度直接影响相移干涉测量的精度。在变频相移干涉测量中,相移量由干涉腔长和波长变化量决定,必须对测试系统进行相位标定,但常因相位标定不精确而引入相移误差,影响相位提取的精度。运用一种基于迭代的相位求解算法,该算法无需对测试系统进行相位标定,可以在被测相位和相移量均未知的情况下,通过交替迭代法求解出被测相位。通过仿真比较了传统四步相移和基于迭代的相位求解算法的相位提取精度,结果表明,基于迭代的相位求解算法的相位提取精度优于传统四步相移算法的精度。     

倾斜相移闭合干涉图的非迭代相位提取方法

针对带倾斜相移误差的闭合干涉图,提出一种非迭代的高精度相位提取方法.该方法用傅里叶变换估计闭合条纹的相位,并用图像分割校正相位的符号,然后利用Zernike多项式拟合确定倾斜相移量,最后用最小二乘拟舍得到高精度相位.数值模拟结果表明:该方法的相位提取误差随着干涉图中条纹根数的增多而减小;当干涉图中条纹根数为4.5时,倾斜相移的估计误差为0.37％.实验结果表明该方法的残余误差均方根值为0.121 7rad.该方法精度高,且无需迭代计算,可应用于相移干涉测量.     

基于扩展平均的多步相移算法及误差抑制特性比较

为满足移相干涉测量中纳米甚至亚纳米高准确度检测要求,需采用对误差敏感度更低的相移算法.基于扩展平均技术,在传统4步和3步算法的基础上分别推导了A类和B类5~13步相移算法公式,以5、6、7和13步算法为例,通过仿真及数值计算,比较了两类算法对相移器移相误差及CCD非线性误差的抑制特性.结果表明:同类算法下,步数越多的算法,对这两项误差的抑制效果越好,但达到一定步数后对测量的影响可以忽略;B类算法具有增强的移相误差抑制能力,在相移不准情况下可优先选用B类算法;A类算法对CCD非线性误差几乎完全免疫,而B类算法受一定CCD非线性误差影响,但对大多数高准确度CCD来说,其在常规检测中的影响可以忽略;由相移噪音引入的随机性测量误差的极大值要略大于相移噪音本身,且不同算法对相移噪音的抑制效果差别不大,因此相移噪音对测量的影响不可忽略.文中给出了不同误差影响下各算法引入波面检测误差的比较数据,研究结果可为实际干涉测量中最适相移算法的使用和选用提供理论指导及数据参考.     

Parallel two-step phase-shifting microscopic interferometry based on a cube beamsplitter

Parallel two-step phase-shifting interferometry for microscopy is presented, and the recording condition for generalized two-step phase-shifting interferometry is discussed. A 45° tilted cube beamsplitter enables to replicate the orthogonally linear polarized object and reference waves into two parallel beams, respectively. As a consequence, two interferograms with quadrature phase shift are obtained along the two beams, and phase reconstructed with an improved algorithm. To reconstruct the phase distribution from the two-step phase-shifting interferograms, a certain recording condition should be satisfied. However, the recording condition has not ever been discussed before. In this paper, the recording condition for the two-step phase-shifting interferometry is derived and that is: the intensity of reference wave should be no less than two times object wave intensity.     

用于相移点衍射干涉仪的加权最小二乘相位提取算法

针对现有的相位提取算法只对某些特定的误差不敏感,不能满足高精度光学检测的要求,本文引入一种等间隔多步移相算法—权重待定的加权最小二乘算法。通过在最小二乘算法中添加待定的权重,分析移相点衍射干涉仪中多种误差源对算法的影响,获得多组约束方程,从而确定权重和新算法。对新算法和标准四步算法、Hariharan五步算法进行比对分析,验证了新算法对PZT线性和二阶非线性移相不准、光强的一阶二阶波动和光源频率一阶二阶波动等误差抑制能力远远优于标准四步算法和Hariharan五步算法;新算法对CCD的量化误差、光强噪声、频率噪声的抑制能力也具有一定优势,且对CCD的二阶响应非线性完全不敏感。     

An improved quantum principal component analysis algorithm based on the quantum singular threshold method

Quantum principal component analysis (qPCA) is a dimensionality reduction algorithm for getting the eigenvectors corresponding to top several eigenvalues of the data matrix and then reconstructing. However, qPCA can only construct the quantum state contains all the eigenvectors and eigenvalues. In this paper, we present an improved quantum principal component analysis (Improved qPCA) algorithm with a fixed threshold. We can reduce the singular value less than the threshold to 0 and obtain a target quantum state which can be used to get an output similar to qPCA after phase estimation. Compared with qPCA, our algorithm has only the target eigenvalues and the probability that we get each target eigenvalue is greater. Furthermore, our algorithm can serve as an additional regularization method and a subroutine for subsequent data processing.     

Multi-focus image fusion based on robust principal component analysis and pulse-coupled neural network

Multi-focus image fusion combines multiple source images with different focus points into one image, so that the resulting image appears all in-focus. In order to improve the accuracy of focused region detection and fusion quality, a novel multi-focus image fusion scheme based on robust principal component analysis (RPCA) and pulse-coupled neural network (PCNN) is proposed. In this method, registered source images are decomposed into principal component matrices and sparse matrices with RPCA decomposition. The local sparse features computed from the sparse matrix construct a composite feature space to represent the important information from the source images, which become inputs to PCNN to motivate the PCNN neurons. The focused regions of the source images are detected by the firing maps of PCNN and are integrated to construct the final, fused image. Experimental results demonstrate that the superiority of the proposed scheme over existing methods and highlight the expediency and suitability of the proposed method.     

基于波长移相剪切干涉的准直波前重构技术

为了实现干涉仪出射准直波前的重构,提出了基于波长调谐移相的横向剪切干涉技术。干涉仪出射波前分别经楔板的前后表面反射,通过角锥棱镜返回后在干涉仪CCD上形成剪切干涉条纹。采用波长移相方法提取剪切干涉条纹的相位信息从而实现准直波前重构。分析相对剪切比对波面重构精度的影响,推导相对剪切比和其影响因素间的关系公式,给出波长移相中光程差常数分量的估算方法。测量干涉仪的三组出射波前,波前的峰谷值分别为3.22λ、2.10λ、0.83λ。该方法简化了传统测量干涉仪准直波前的横向剪切干涉装置,提高了测量精度,特别适合于测量波长移相干涉仪的出射波前。     

Kernel principal component analysis for stochastic input model generation

Stochastic analysis of random heterogeneous media provides useful information only if realistic input models of the material property variations are used. These input models are often constructed from a set of experimental samples of the underlying random field. To this end, the Karhunen–Loève (K–L) expansion, also known as principal component analysis (PCA), is the most popular model reduction method due to its uniform mean-square convergence. However, it only projects the samples onto an optimal linear subspace, which results in an unreasonable representation of the original data if they are non-linearly related to each other. In other words, it only preserves the first-order (mean) and second-order statistics (covariance) of a random field, which is insufficient for reproducing complex structures. This paper applies kernel principal component analysis (KPCA) to construct a reduced-order stochastic input model for the material property variation in heterogeneous media. KPCA can be considered as a nonlinear version of PCA. Through use of kernel functions, KPCA further enables the preservation of higher-order statistics of the random field, instead of just two-point statistics as in the standard Karhunen–Loève (K–L) expansion. Thus, this method can model non-Gaussian, non-stationary random fields. In this work, we also propose a new approach to solve the pre-image problem involved in KPCA. In addition, polynomial chaos (PC) expansion is used to represent the random coefficients in KPCA which provides a parametric stochastic input model. Thus, realizations, which are statistically consistent with the experimental data, can be generated in an efficient way. We showcase the methodology by constructing a low-dimensional stochastic input model to represent channelized permeability in porous media.     

优化的两步相移算法在图像加密中的应用研究

优化了此前提出的两步相移算法,仅通过两幅去除背景光强(或抑制直流分量)后的干涉图数据和一个(0,π)区间的相移值,就可以成功再现出原始物波场信息,无需借助于物光光强、参考光光强等其他辅助测量信息.与菲涅耳域的双随机相位编码技术结合,该优化算法可以应用于图像加密方案中,通过计算机仿真实验验证了所提方案的可行性,并对几何密钥的灵敏度进行了测试分析.     

基于主分量分析法的小波域三维目标序列图像隐藏技术

针对在一幅载体图像中隐藏三维目标序列图像的问题,本文利用主分量分析法获取三维目标的本征图像,将本征图像的小波域系数嵌入到载体图像的小波域系数中,利用分解系数和提取得到的本征图像重构出三维目标的系列图像.本文所提出的方法不是直接存储目标图像,而是存储能够反映三维目标特征的一组本征图像.研究结果表明,该方法有效地将三维目标的特征隐藏在了载体图像中,隐藏信息量大. 关键词： 信息隐藏 三维目标 主分量分析 小波变换     

基于KPCA的氧碘激光器出光过程监控方法

根据氧碘化学激光器运行过程基本稳定的特点,利用核主元分析(KPCA)方法监控非线性过程的优势,提出了基于KPCA的激光器出光过程监控方法。该方法通过沿时间轴方向展开激光器正常出光的历史数据,建立了激光器出光过程中各时刻的KPCA模型,并在特征空间构建T2统计量和平方预测误差统计量对激光器出光过程进行监控。仿真计算和试验表明,该方法具有可靠的监控性能,能及时、准确地发现激光器出光过程中的异常状态。