多尺度气泡尺寸分布数字图像测量方法研究

鼓泡塔是一种广泛应用于能源和环境领域的多相流反应器,鼓泡塔中气泡的大小和浓度对于研究鼓泡塔中"三传一反"过程具有重要意义。采用高速摄像法和数字图像处理技术开展了鼓泡塔中内多尺度气泡尺寸分布测量研究,针对气泡识别过程中密集气泡易发生重叠的问题,提出基于曲率计算的凹点匹配与圆周拟合的重叠气泡分割与轮廓重构算法。搭建了鼓泡塔反应器实验装置,针对星型、均匀和方形3种不同进气孔形态的气泡分布器开展了实验研究,分析了不同尺度气泡的尺寸分布规律。试验结果表明:该算法不仅能够有效地从图像中提取轮廓清晰完整的气泡,而且能够对图像粘连重叠的气泡进行准确分割,从而可精确地获得多尺度气泡尺寸分布。随着气体流量的增加,小气泡的数量急剧增加,同时产生更大的气泡;气泡的最大直径和Sauter平均直径均随气体流量的增加而增大,且两者的比值基本保持不变,即分布器形式对气泡尺寸分布均匀性有影响,方形分布器产生气泡最均匀,气含率相对其他两种分布器更高。实验结果证明了图像分割与轮廓重构方法在气液两相流中气泡参数在线测量的可行性。     

显微图像的光学薄膜缺陷密度统计

薄膜表面缺陷密度统计是改进薄膜表面质量的重要依据。阐述了基于遗传算法的二维最大熵分割算法的原理及实现步骤。采用这种算法对薄膜缺陷图像进行分割,对分割后的图像进行了薄膜缺陷密度的测量。实验结果表明,这种方法对薄膜表面缺陷提取简单且易于测量,为分析缺陷原因提高薄膜质量起到重要的指导作用。     

室内可吸入颗粒物浓度与粒径分布检测方法的研究

本文采用显微观测成像和数字图象处理技术,研究了一种室内可吸入颗粒物浓度与粒径分布的检测方法.基于数学形态学对颗粒物图像进行图像处理,设计了检测颗粒物粒径,分形维数和形状因子等重要形态学参数的识别算法,最后采用数据融合的方法计算了可吸入颗粒物的浓度及其粒径分布.实验结果表明:这种检测方法可同时得到可吸入颗粒物的数量浓度、总表面积和质量浓度及其粒径分布,具有处理速度快、数据统计分析方便快捷、检测结果稳定等优点.     

混凝土结构裂缝宽度波前编码测量系统的设计

混凝土结构裂缝宽度测量对监测建筑结构的健康状态、避免混凝土结构损坏性缺陷具有十分重要的意义。为了克服基于传统光学成像技术设计的手持式混凝土裂缝宽度测量系统焦深小准确调焦困难,以及由于存在像差和混凝土表面凹凸过大致使视场内图像无法完全清晰等缺陷,采用波前编码技术重新进行缝宽检测系统的设计,以便有效地拓展景深（焦深）。首先,讨论了波前编码立方型相位板规一化参数及其与实际系统相位板设计参数之间的关系,接着利用CODE V光学设计软件,完成了手持式裂缝宽度波前编码测量系统的设计,然后加工制作了立方型相位板并进行了初步实验。实验表明,采用基于单透镜的波前编码系统设计,在满足50mm分辨率、F数为6的前提下,可将景深拓展为15mm,达到传统光学系统相同F数时景深理论值的4.5倍。该系统能够满足混凝土结构裂缝宽度测量中扩大景深和成像的基本要求。     

铁谱磨粒图像多焦点融合算法研究

铁谱分析技术是一种常用的磨损监测技术。受限于高倍物镜下的景深限制,一张铁谱大磨粒图像往往只有局部聚焦清晰的特征。为了能够解决在高倍物镜下铁谱大磨粒图像的自动化清晰采集以及高质量图像融合问题,设计并构建一套自动化扫描显微系统,该系统可进行多焦点铁谱图像的自动扫描采集;同时,提出一种基于相位一致性的铁谱磨粒图像多焦点融合算法,对自动扫描的多焦点图像进行融合,得到清晰的磨粒图像。实验结果表明,设计的自动化扫描显微系统能快速完成多焦点铁谱图像的自动化采集流程,提出的图像融合算法相较于传统的小波图像融合算法具有更高的图像评价质量,并能获得更加清晰的图像边缘信息。     

基于FFT的动态散射光谱密度测量技术研究

为了研究动态光散射纳米颗粒测量技术,提出了基于FFT的动态散射光AR模型功率谱密度测量法。首先采用FFT建立信号的AR功率谱密度模型,然后通过计算信号自相关矩阵的秩得到AR模型的阶数P,由于任何信号的自相关与其频域的功率谱密度是一个傅里叶变换对,因此,信号功率谱密度的衰减宽度等价于其自相关函数的衰减宽度,这样通过计算动态光散射信号的功率谱密度衰减宽度,进而得到待测颗粒的粒径,最后将此方法的实验结果与传统的光子相关光谱法实验所测得的数据进行了比较。结果表明：该技术具有误差小、重复性好的优势,有着很大的实用价值。     

气泡影响下的电磁流量测量优化技术研究

电磁流量测量在工业生产过程中扮演着重要角色,但易受流体中气泡的影响导致测量结果出现波动进而影响测量精度,因此通过技术手段实现测量精度的优化十分关键。针对电磁流量测量精度受气泡影响的测量优化问题,本文首先从权重函数角度入手,建立了气泡对电磁流量测量影响的理论模型;其次,通过有限元仿真研究了气泡对权重函数的影响,并根据仿真结果提出了一种基于图像采集与处理技术的优化方法降低气泡对电磁流量测量的影响;最后,为了验证优化方法的可行性,开发了气泡图像处理算法,并搭建气液两相流流体电磁流量测量实验平台进行实验验证。实验结果表明,采用优化方法补偿后的电磁流量测量系统受气泡影响的敏感程度得到有效降低,误差降低幅度均在82.63%以上,最大误差降低幅度可达91%,优化后气泡存在时的测量误差在±3.03%以内。研究有效降低了电磁流量测量受气泡影响产生的误差,为进一步提高气泡影响下的电磁流量测量精度和实现气液两相流电磁测量提供技术支持。     

基于双谱检测法提取目标红外图像特征

在双谱原理的基础上,推导了圆周积分双谱变换算法,分析了圆周积分双谱算法及其性质,讨论了基于双谱检测法提取目标红外图像特征的算法。与普通的傅里叶变换相比较,双谱保留了除线性相位以外的全部信号信息,而前者的幅度特征丢失了图像的全部相位,作为图像模式识别的形状信息一般更多地包含在相位而不是幅度之中。因此,圆周积分双谱能够很好地反映目标的形状特征信息。通过大量的试验图像数据验证了不同算法对于目标特征提取的特点。     

基于光流法的气密性检测装置研究

针对用气泡检测小型压力容器气密性的方法存在工作效率低下以及较多的安全隐患等问题,进行了气泡图像处理法的研究。搭建了气泡图像采集系统。利用Matlab软件结合光流算法处理气泡图像,研究了基于光流理论的Horn-Schunck气泡图像处理算法。对气泡图像进行了定制阈值的图像区域分割,获得了层次分明的气泡图像;基于此方法设计了一套半自动化的气密性检测系统。利用此系统自动得到了气泡的数量和大小并以此计算泄漏量以及泄漏的部位。实验结果表明:此系统可准确检测的最小气泡直径为4 mm,气泡检测最适合的直径在8 mm左右,如气泡直径继续增大,气泡表面不规则程度加深,检测精确度变低,因此,该系统在一定的条件下能准确计算气泡数量及大小,实现了对泄漏气体的检测。     

基于蒙特卡罗模型的高浓度浆液细度在线测量

近年来颗粒粒径测量问题受到了国内外的广泛研究。 针对燃煤电厂高浓度脱硫石灰石浆液细度在线测量,将基于蒙特 卡罗方法的声衰减模型拓展至高浓度范围,高浓度蒙特卡罗模型(HC-MCM)预测衰减系数与实验测量值相吻合。 构建 HC-MCM 矩阵用于颗粒粒径分布的反演可减小计算时间,且差分进化算法的计算结果与设定粒径和分布宽度的相对误差小于 1% 。 搭建基于透射和反射法的双模式超声检测装置并开发软件测量系统,加工适用于高浓度浆液细度测量的透射传感器和超声探 针,依托燃煤电厂完成长达 168 h 的浆液细度在线测量实验。 结果表明,两种模式超声衰减谱的变化趋势和数值大小基本吻 合。 粒径分布参数 DV50 与激光粒度仪测量结果的相对误差小于 8% ,其粒径分布满足燃煤电厂成品石灰石浆液的细度要求。     

Gradient based intensity normalization

Intensity normalization is important in quantitative image analysis, especially when extracting features based on intensity. In automated microscopy, particularly in large cellular screening experiments, each image contains objects of similar type (e.g. cells) but the object density (number and size of the objects) may vary markedly from image to image. Standard intensity normalization methods, such as matching the grey-value histogram of an image to a target histogram from, i.e. a reference image, only work well if both object type and object density are similar in the images to be matched. This is typically not the case in cellular screening and many other types of images where object type varies little from image to image, but object density may vary dramatically. In this paper, we propose an improved form of intensity normalization which uses grey-value as well as gradient information. This method is very robust to differences in object density. We compare and contrast our method with standard histogram normalization across a range of image types, and show that the modified procedure performs much better when object density varies between images.     

电路噪声对星敏感器星点定位精度的影响

提出了一种测量星敏感器电路模块带来的图像噪声的方法。在实验室条件下,用星敏感器拍摄多幅背景图像,分析表明:不同背景图像上相同像素的灰度差值代表了电路模块带来的图像噪声,该噪声服从正态分布。模拟产生该正态分布的随机矩阵作为图像噪声加到理想的星点上,采用亚像素细分质心算法计算出带噪声的星点质心坐标,从而得到噪声带来的星点位置偏差。计算结果表明:实验室条件下,星敏感器实测6等星的质心测量精度为1/25 Pixel,而电路噪声将引起1/200 Pixel量级的标准偏差;电路噪声,尤其是DC/DC电源纹波噪声对星敏感器星点定位带来的误差是不容忽视的。     

Measurement of potential distribution function on object surface by using an electron microscope in the mirror operation mode

The quantitative theory of image contrast in an electron microscope in the mirror operation mode is given in this paper. This theory permits us to calculate the potential distribution on the object surface from the current density distribution on the microscope screen. The potential distribution results in image formation on the screen. Local electric fields existing on the object surface lead to a perturbation of electron trajectories above the object and to a redistribution of the current density on the screen, causing image contrast. Using the quantitative correlation between these fields and the function of current density distribution on the screen, it is possible to calculate the magnitude of these microfields as well. As illustration, a measured potential distribution on an object surface with spiral structures of adsorbates was analysed. These structures are formed during reaction of CO oxidation on Pt(110). The value of the measured contact potential difference comprised a few hundredths of volt.     

梯度引导电学成像自适应网格生成方法

电学层析成像是一种观测场域内电导率分布的无损检测技术。有限元法是求解电学层析成像问题的常用方法。其作为线性化的近似方法,剖分单元的大小会影响有限元法求解的精度。更密的尺寸可以提高重建图像的空间分辨率,但会增加计算成本,同时未知量个数的增加会加剧逆问题的欠定性。针对上述问题,提出一种基于图像梯度的自适应网格生成方法。根据初始重建图像的梯度,自适应地提高内含物区域的网格密度,降低其他区域的网格密度,并对场域边界进行精确拟合来优化被测场域的网格剖分。通过仿真与实验研究对比分析了所提方法与常用网格剖分方法。结果表明,所提方法的重建结果图像误差平均降低15%,相关系数平均提高7%,因此所提方法在不显著增加或减少网格数的情况下,可以有效提高内含物的重建精度和图像重建质量。     

细粒度金刚石砂轮形貌测量与评价

采用基于扫描白光干涉原理的三维表面轮廓仪对粒度为3 000的金刚石砂轮表面形貌进行测量,其图像拼接功能可以确保较高的横向分辨率、较高的垂直分辨率和较大的取样面积。利用频谱分析方法对砂轮表面的频率构成进行分析,通过理想的低通数字滤波消除测量仪器引起的系统噪声和砂轮表面的高频分量,然后重建砂轮表面的三维形貌,在此基础上得出砂轮的磨粒出刃高度、静态有效磨粒密度、磨粒平均间距。研究表明,采用细粒度金刚石砂轮进行超精密磨削时,磨粒出刃高度大体上服从正态分布,静态有效磨粒密度远低于理论磨粒密度,真正起切削作用的磨粒数量极少。     

Photon event distribution sampling: an image formation technique for scanning microscopes that permits tracking of sub-diffraction particles with high spatial and temporal resolutions

In photon event distribution sampling, an image formation technique for scanning microscopes, the maximum likelihood position of origin of each detected photon is acquired as a data set rather than binning photons in pixels. Subsequently, an intensity-related probability density function describing the uncertainty associated with the photon position measurement is applied to each position and individual photon intensity distributions are summed to form an image. Compared to pixel-based images, photon event distribution sampling images exhibit increased signal-to-noise and comparable spatial resolution. Photon event distribution sampling is superior to pixel-based image formation in recognizing the presence of structured (non-random) photon distributions at low photon counts and permits use of non-raster scanning patterns. A photon event distribution sampling based method for localizing single particles derived from a multi-variate normal distribution is more precise than statistical (Gaussian) fitting to pixel-based images. Using the multi-variate normal distribution method, non-raster scanning and a typical confocal microscope, localizations with 8 nm precision were achieved at 10 ms sampling rates with acquisition of ~200 photons per frame. Single nanometre precision was obtained with a greater number of photons per frame. In summary, photon event distribution sampling provides an efficient way to form images when low numbers of photons are involved and permits particle tracking with confocal point-scanning microscopes with nanometre precision deep within specimens.     

A deep neural networks-based image reconstruction algorithm for a reduced sensor model in large-scale tomography system

Image reconstruction for soft-field tomography is a highly nonlinear and ill-posed inverse problem. Owing to the highly complicated nature of soft-field, the reconstructed images are always poor in quality. One of the factors that affect image quality is the number of sensors in a tomography system. It is commonly assumed that increasing the number of sensors in a tomography system will improve the ill-posed condition in image reconstruction and hence improve image quality. However, as the number of sensors increases, challenges such as more complicated and expensive hardware, slower data acquisition rates, longer image reconstruction times, and larger sensitivity matrices will arise, resulting in a greater ill-posed condition. Since deep learning (DL) is capable of expressing complex nonlinear functions, the majority of research efforts have been directed toward developing a robust DL-based inverse solver for image reconstruction. However, no study has been conducted to solve the inverse problem and improve the quality of the reconstructed image using a reduced sensor model for a large-scale tomography system. This paper proposed an image reconstruction algorithm based on Deep Neural Networks (DNN) to investigate its feasibility in solving the ill-posed inverse problem caused by the reduced sensor model for a large-scale tomography system. The proposed DNN model is based on a supervised, feed-forward, fully connected, backpropagation network. It comprises an input layer, three hidden layers and an output layer. Also, it was trained using large data samples obtained from COMSOL simulation. The relationship between the scattered electromagnetic field measurement and the corresponding true electromagnetic field distribution vector is determined. During the image reconstruction process, the untrained scattered electromagnetic field measurement samples are used as inputs to the trained DNN model, and the model output is an estimate of the electromagnetic field distribution. The results show that the proposed DNN can accurately describe the distribution of electromagnetic field and boundary shape of phantom compared to traditional algorithms (LBP, FBP, Noser and Tikhonov), regardless of the size and number of phantoms within the monitoring area. Hence, the proposed DNN is more robust and has a high degree of generalization.     

Determination of vesicle size distributions by freeze-fracture electron microscopy

The most common electron microscopic technique for obtaining information on size distributions of uncollapsed membrane vesicles is based on the method of van Venetie (1980). This technique involves the sizing of only those vesicles that were freeze fractured at their equatorial planes. As a result, only a small number of images can be used to generate size distributions. Further, the technique is susceptible to systematic error. An alternate approach is to consider the complete distribution of image sizes and use this distribution to determine the average size and distribution of the vesicles. It is shown that the mean vesicle size is 4/π times the mean image size. As well, a parameter, m, which can be determined from the image distribution, can be used to characterize the vesicle distribution. The advantage of this new approach is that images of all vesicles are used, leading to a statistically better determination of vesicle sizes.     

Study of grey relational grade identification for ferrography based on characteristic analysis of wear debris

Attention has been focused on how to achieve intelligent automation in ferrographic diagnosis in order to overcome the subjectivity of the diagnosis process. The present paper reports on a technique of characteristic measurement developed on the basis of the VC++ 6.0 programming platform, with characteristic parameters such as area, roundness, and aspect ratio being extracted from images of wear debris based on digital image analysis. However, the extraction of characteristic parameters from a ferrographic image is not the ultimate purpose of ferrographic diagnosis. The wear particles should be classified into several pre‐decision categories and their statistical distribution should also be calculated. The grey relational grade theory is introduced in this paper as a way to recognise wear debris and a new software system has been developed to deal with the problems occurring in the automation of ferrographic diagnosis. It is shown that the identification rules can be used to treat some real wear debris images with generally satisfactory results.     

Imaging and 3D morphological analysis of collagen fibrils

The recent booming of multiphoton imaging of collagen fibrils by means of second harmonic generation microscopy generates the need for the development and automation of quantitative methods for image analysis. Standard approaches sequentially analyse two-dimensional (2D) slices to gain knowledge on the spatial arrangement and dimension of the fibrils, whereas the reconstructed three-dimensional (3D) image yields better information about these characteristics. In this work, a 3D analysis method is proposed for second harmonic generation images of collagen fibrils, based on a recently developed 3D fibre quantification method. This analysis uses operators from mathematical morphology. The fibril structure is scanned with a directional distance transform. Inertia moments of the directional distances yield the main fibre orientation, corresponding to the main inertia axis. The collaboration of directional distances and fibre orientation delivers a geometrical estimate of the fibre radius. The results include local maps as well as global distribution of orientation and radius of the fibrils over the 3D image. They also bring a segmentation of the image into foreground and background, as well as a classification of the foreground pixels into the preferred orientations. This accurate determination of the spatial arrangement of the fibrils within a 3D data set will be most relevant in biomedical applications. It brings the possibility to monitor remodelling of collagen tissues upon a variety of injuries and to guide tissues engineering because biomimetic 3D organizations and density are requested for better integration of implants.