Digital image processing for nonperiodic objects and an on-line image processing system for high resolution electron microscopy

A digital image processing method for noise removal and image enhancement in nonperiodic structural images is described. The method for noise removal uses a reversible transform between an image and image autocorrelation function. The Laplacian filter is then employed for image enhancement. Furthermore, an on-line image processing system for highresolution TEM is presented.     

Real-space filtering of electron images from optical autocorrelation patterns.

The capability for filtering electron images, in real space, is demonstrated to be inherent to a two plate, incoherent optical procedure (originally described by Meyer-Eppler and Darius) for recording autocorrelation functions from transparencies. If an image contains features that occur with more than random frequency, these features may give rise to a resolvable peak in the optically recorded autocorrelation function. The optical requirements for obtaining an image of the transparency that is filtered to observe only features giving rise to some set of such peaks, or to exclude them, are described. The principle is to form an image of the transparency, with a properly placed plano-convex lens, from the incoherent light transmitted through apertures positioned over the peaks in the autocorrelation plane. The application of the method in defining the position and orientation of specific projections of protein molecules, as observed in negative stain by bright-field, or unstained by dark-field electron microscopy, is also described.     

Quantitative comparison of images and transforms

Many different measures are currently used for quantifying the difference between pairs of images. Those in common use are surveyed, their interrelationships are made clear and their dependence on transform band-width pointed out. Adaptations to image transforms, with complex data, are given. For comparing simulated with experimental images, the χ² difference is recommended as allowing a simple prediction of the likelihood of obtaining at random a difference as great as that actually obtained. In particular, the χ² difference in Fourier space is shown to be the only interpretable difference measure available in cases when the image noise is not normally distributed or when — as is normally the case — the noise in neighbouring image pixels is not independent.     

A New Method for Rough Surface Profile Simulation Based on Peak–Valley Mapping

Starting from random process theory and taking peaks and valleys as reconstruction carriers to replace surface height distribution and autocorrelation function (ACF), a new method for rough surface simulation is proposed based on the peak–valley mapping principle. The proposed method is used to simulate rough surfaces with different correlation lengths. The simulated roughness parameters and ACF are examined. Comparisons of simulation precision and simulation efficiency between the proposed method and fast Fourier transform (FFT) are provided. Finally, the applications of the method are discussed briefly.     

基于傅里叶变换和Gyrator变换的图像加密

基于傅里叶变换和Gyrator变换对图像进行加密。将原始图像和第一个随机相位函数叠加后做傅里叶变换,然后将频域信息减去第二个随机相位函数后得到一个复函数。复函数经过Gyrator变换得到加密图像,将第二个随机相位函数作为相位密钥,同时将Gyrator变换角度作为密钥,由此增大了密钥空间和增强了系统安全性。通过数字方法对图像进行加密,解密过程用光学装置实现。计算机模拟结果表明,该加密方法解密图像质量好,系统安全性良好。     

Automatic focusing by correlative methods

The possibility of focusing images by the autocorrelation function is explained. It can be shown that these techniques are less sensitive to disturbances by noise than others. Furthermore, focusing criteria derived from autocorrelation functions have different responses to image contrast. It has been shown that these focusing criteria can be determined using binary images and applying the laws of stochastic ergodic metrology. This leads to a large reduction in computing time. Moreover, an attempt was made to focus by means of binary images determined by simple segmentation. The experimental results show that such a focus criterion operates quite well. The criterion offers the advantage that brightness levels in the image can be chosen selectively for focusing.     

A method for personal expertise-independent evaluation of image resolution in scanning electron microscopy

The two conventional methods currently employed for the evaluation of image resolution in scanning electron microscopy are the gap method and a fast Fourier transform (FFT) method. These can be highly dependent on personal expertise on the distinction between signal information and noise contained in a micrograph. Hence, the present paper proposes an alternative method (referred to as a contrast-to-gradient (CG) method) that can determine the image resolution of a micrograph without requiring personal expertise on the judgment of noise. The image resolution in the CG method is defined as a weighted harmonic mean of the local resolution, which is proportional to the quotient of the threshold contrast divided by the local gradient. The local gradient is calculated from the quadratic function that best fits the local pixel intensities over 5 x 5 pixels. It has been shown that the CG method, compared with the FFT method, has a broader range of applications for various types of images, such as low-contrast, noise-containing, filter-processed, highly directional, and quasi-periodic feature images.     

齿面干涉图像的相位解包裹算法对比研究

齿面干涉图像具有局部散斑、边缘杂散条纹、疏密不均和条纹粘连等噪声特征。为了寻找适用于处理齿面干涉图像噪声特征的解包裹算法,选取枝切法、相位导数偏差法的质量图引导法、最小二乘法、傅里叶变换轮廓术以及傅里叶变换的质量图引导法等 5 种算法,分析其对齿面干涉图像噪声处理的解包裹特点,通过对比分析解包裹相位图以及结果图中相位数据曲线,得出相位导数偏差的质量图引导法相比于其他算法解包裹精度高,能较好地抑制因噪声严重导致的解包裹相位突变、连续波动等问题。     

Practical implementation of a direct method for coherent diffractive imaging

We experimentally implement a direct, non-iterative method for recovering the complex wave in the exit-surface plane of a coherently illuminated object. The form of illumination is subject to certain conditions. By satisfying these conditions, the complex exit-surface wave is directly recovered from a single far-field intensity pattern, by solving a set of linear equations. These linear equations, whose coefficients depend on the incident illumination, are obtained by analyzing the autocorrelation function of the exit-surface wave. This autocorrelation is constructed by taking the inverse Fourier transform of the diffraction pattern. We introduce a preconditioning step, for the system of linear equations, which improves the robustness of the method to noise. While the present experimental proof of concept has been performed using a visible-light laser, the method is applicable to diffractive imaging using coherent X-ray and electron sources.     

游梁式抽油机冲次软测量方法研究

游梁式抽油机冲次是影响抽油机系统运行工况的重要参数,实时精确地测量抽油机冲次是调控及优化抽油机系统效率的基础。针对现有公式法和传感器法测量冲次,存在准确性易受实际运行条件和野外环境影响、需额外增设测量器件、故障率较高等缺陷,提出一种基于自相关理论的抽油机冲次软测量方法。即建立冲次与悬点载荷函数、驱动电机输入电功率函数的周期关联关系模型,通过采集驱动电动机输入电流、电压计算实时输入电功率,利用周期信号自相关函数与原函数同周期、噪声信号自相关函数在原点集中的特性,对输入电功率函数进行自相关运算,经去除干扰后获得电功率函数周期,依据输入电功率函数周期与抽油机冲程周期、抽油机冲次的关联关系计算得到抽油机冲次,从而在不增加外设条件下实现对抽油机冲次的软测量。仿真以及某大型油田采油六大队现场试验表明,该方法实用有效、抗扰性高、测量误差不超过1%。     

Single-image signal-to-noise ratio estimation

A method for estimating the signal-to-noise ratio from a single image is presented in this paper. The autocorrelation-based technique requires that image details be correlated over distances of a few pixels, while the noise is assumed to be uncorrelated from pixel to pixel. The latter is shown to be a good approximation in the case of scanning electron microscope (SEM) images provided that the video signal is not band limited. The noise component is derived from the difference between the image autocorrelation at zero offset and an estimate of the corresponding noise-free autocorrelation. Nonlinear effects introduced by intensity saturation and their implications on the image signal-to-noise ratio are also discussed.     

基于S变换的热连轧机耦合振动特征提取

提出一种基于短时傅里叶变换的自适应频域滤波方法,将噪声信号与振动特征成功地分离。根据短时傅里叶变换和功率法设定的阀值,自动捕捉了振动信号在不同时间段的优势频率。对振动信号、压下液压缸压力信号和伺服阀给定信号做短时傅里叶变换后,热连轧机振动被诊断为液机耦合振动。利用离散小波变换和S变换相结合的方法对轧机振动信号进行分析,确定轧机起振的时间为液压压下系统的投入时间,证明了热连轧机存在液机耦合振动现象。     

调频加网视觉噪声检测与降噪方法研究

调频网印品的视觉噪声即颗粒性(感),是限制调频网工艺推广和应用的关键问题之一.视觉噪声源自网点分布的无规则,与网点大小、形状变化及网点间距离不均匀有关.为此以点阵数字图像为检测对象,提出网点粘接度、网点周长及网点分布密度均方差等算法,结合付里叶变换分析检测调频网的噪声,指出保持网点大小一致、采用均匀分布和误差扩散相结合的算法,可有效降低调频网印品的视觉噪声.     

零件图像的Hilbert扫描-小波分解去噪处理

提出了利用图像的Hilbert扫描曲线和小波变换实现图像去噪的方法.将含噪声图像生成为Hilbert扫描矩阵,再将Hilbert扫描矩阵转换为一维向量,对一维向量进行小波分解,提取低频分量并转换为二维矩阵.然后,对二维矩阵进行Hilbert反扫描,完成图像去噪处理.仿真实验结果表明该方法是有效的.     

Direct exit-wave reconstruction from a single defocused image

We propose a direct, non-iterative method for the exact recovery of the complex wave in the exit-surface plane of a coherently illuminated object from a single defocused image. The method is applicable for a wide range of illumination conditions. The defocus range is subject to certain conditions, which if satisfied allow the complex exit-surface wave to be directly recovered by solving a set of linear equations. These linear equations, whose coefficients depend on the incident illumination, are obtained by analyzing the autocorrelation function of an auxiliary wave which is related to the exit-surface wave in a simple way. This autocorrelation is constructed by taking the inverse Fourier transform of the defocused image. We present an experimental proof of concept by recovering the exit-surface wave of a microfiber illuminated by a plane wave formed using a HeNe laser.     

基于正交小波变换的计权声级测量

提出了采用正交小波变换进行计权声级测量的方法 ,克服了传统计权声级测量系统中采用模拟计权网络的缺点 ,比采用短时傅里叶变换方法更能反映人耳对噪声的主观感觉特性。理论分析和仿真计算表明 ,该方法是可行的。为了进一步提高计算精度和减少计算复杂度 ,给出了对应于 IIR滤波器的正交小波变换、自适应小波包变换和匹配小波变换等三种可能的改进方法。     

Deconvolution of Stark broadened spectra for multi-point density measurements in a flow Z-pinch

Stark broadened emission spectra, once separated from other broadening effects, provide a convenient non-perturbing means of making plasma density measurements. A deconvolution technique has been developed to measure plasma densities in the ZaP flow Z-pinch experiment. The ZaP experiment uses sheared flow to mitigate MHD instabilities. The pinches exhibit Stark broadened emission spectra, which are captured at 20 locations using a multi-chord spectroscopic system. Spectra that are time- and chord-integrated are well approximated by a Voigt function. The proposed method simultaneously resolves plasma electron density and ion temperature by deconvolving the spectral Voigt profile into constituent functions: a Gaussian function associated with instrument effects and Doppler broadening by temperature; and a Lorentzian function associated with Stark broadening by electron density. The method uses analytic Fourier transforms of the constituent functions to fit the Voigt profile in the Fourier domain. The method is discussed and compared to a basic least-squares fit. The Fourier transform fitting routine requires fewer fitting parameters and shows promise in being less susceptible to instrumental noise and to contamination from neighboring spectral lines. The method is evaluated and tested using simulated lines and is applied to experimental data for the 229.69 nm C III line from multiple chords to determine plasma density and temperature across the diameter of the pinch. These measurements are used to gain a better understanding of Z-pinch equilibria.     

Analysis of scanning probe microscope images using wavelets

The utility of wavelet transforms for analysis of scanning probe images is investigated. Simulated scanning probe images are analyzed using wavelet transforms and compared to a parallel analysis using more conventional Fourier transform techniques. The wavelet method introduced in this paper is particularly useful as an image recognition algorithm to enhance nanoscale objects of a specific scale that may be present in scanning probe images. In its present form, the applied wavelet is optimal for detecting objects with rotational symmetry. The wavelet scheme is applied to the analysis of scanning probe data to better illustrate the advantages that this new analysis tool offers. The wavelet algorithm developed for analysis of scanning probe microscope (SPM) images has been incorporated into the WSxM software which is a versatile freeware SPM analysis package.     

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.     

应用半二次罚函数的图像盲去模糊

由于现有的模糊图像盲恢复算法计算复杂度高,计算量大,本文提出了一种基于半二次罚函数的图像盲去模糊算法,并进行了实验验证。应用图像噪声的多阶偏导数的高斯分布特性和图像梯度值服从hyper-Laplacian分布特性建立方程,使用高效交替迭代的算法对方程求解。由于迭代过程中采用快速傅里叶变换一次求解,故大大降低了运算时间,同时获得了很好的恢复效果,为实现实时视频图像去模糊奠定了基础。对一个百万像素级的图像进行了去模糊实验,结果显示,本文算法比当前流行的算法有更快的计算速度和更好的鲁棒性,计算时间缩短了60%。提出的算法为视频图像的实时盲恢复提供了新的工具。