离焦模糊图像清晰度评价函数的研究

离焦模糊图像清晰度评价函数是采用数字图像处理技术实现显微镜自动调焦的一个关键,也是图像恢复中评价图像质量的一个重要判据.本文首次提出了用图像灰度梯度向量模方和、Roberts梯度和与拉普拉斯(8邻域微分)算子和作为由于离焦产生的模糊图像的评价函数.文中分别建立了上述几种评价函数的数学模型,给出了实验结果和分析.与以往的图像灰度熵和图像灰度方差评价函数相比,本文给出的评价函数无偏性好、单峰性强,在焦平面附近具有变化趋势明显和灵敏度高的特点.     

基于图像梯度差值算法的自动快速调焦

为了提高自动调焦技术的测量精度,采用图像梯度差值算法。首先建立调焦窗口,高斯型非均匀采样函数使远离中心区域的分辨率降低,接着增设阈值改进黄金分割的调焦搜索,最后在梯度差值中对图像的一个像素以及其周围的八相邻像素进行考虑。构造像素的最大灰度梯度和最小灰度梯度,孤立噪声点减弱评价函数的影响,使图像边缘点近似等于最大像素灰度梯度值的平方。实验仿真显示系统自动调焦精度达±0.1μm,满足测量系统对精度、稳定性和实时性上的要求。     

自动聚焦技术在刀具测量仪器中的应用

在光学测量仪器中,如何提高聚焦精度是非常重要的。本文针对刀具几何参数测量仪,设计了自动聚焦系统。提出了以步进电机驱动显微镜移动,结合图像处理技术来实现自动聚焦的方法。通过对比实验,选择了图像的灰度梯度向量模方和算子作为图像是否聚焦的评价函数,以及给出了登山式自动聚焦策略。最后对该系统做了重复性聚焦实验,证明上述方法可以提高系统的聚焦精度。     

电子摄像式刀具预调测量仪的调焦方法研究

电子摄像式刀具预调测量仪中,测量图像具有背景部分灰度值高、刀具部分灰度值低、边缘部分处于中间灰度值的特点。由于图像越正焦,边缘梯度越大,处于中间灰度值的像素数越少,因此提出了一种基于中间灰度值像素数统计的粗调焦评价方法,当值达到极小值时认为图像正焦。该方法简单,只统计像素个数,所以具有较快的处理速度,实验结果表明该方法对于透射式照明图像测量系统来说具有较大的调焦范围,适合于粗调焦。     

基于邻域特性分析的小模数齿轮亚像素图像边缘检测

针对高分辨率小模数齿轮图像中边缘灰度梯度呈双峰或多峰分布的问题,提出一种基于邻域特性分析的小模数齿轮图像亚像素边缘检测算法。算法以邻域窗口的灰度均方差积表示边缘强度、灰度重心所在的方向表示灰度变化的方向;通过阈值化、非极大值抑制和边缘细化等获取初始边缘;在初始边缘的基础上将求取的灰度变化方向划分为八个区域,构建一维灰度矩模型解算亚像素边缘位置。为验证算法的有效性,分别以模拟图像与实际图像为对象进行亚像素边缘检测实验,根据齿轮边缘特性以最小二乘拟合法评价算法对齿轮图像的检测效果。实验结果表明,该算法具有曝光敏感度优、绝对定位精度高、重复性好的优点。     

基于灰度线性建模的亚像素图像抖动量计算

为了解决凝视遥感云场景序列图像的亚像素抖动量求解问题,提出了基于灰度线性建模的亚像素序列图像抖动量计算方法。首先,利用三参数线性模型描述像素及邻域灰度,提出了一种图像灰度的线性建模方法。其次,以序列帧图像相对参考帧图像的抖动量作为线性模型中的优化变量,以参考帧图像与序列帧图像之间的相似性为优化目标,建立了亚像素抖动量解算的最小二乘优化方法,并推导得到了解析计算公式。最后,利用云场景序列图像进行了算法仿真验证。结果表明,该方法抖动量的计算误差小于0.1pixel。将该方法与传统基于特征点的配准算法进行了比较,结果显示该方法具有较高的抖动量计算精度,可应用于遥感图像几何定标、目标定位以及时序图像中目标多帧关联检测等。     

显微镜自动聚焦算法的研究

自动聚焦技术是全自动控制显微镜系统中的核心部分。自动聚焦算法综合了快速灰度差分法和高精度拉普拉斯函数法的优点,根据聚焦过程中各阶段聚焦评价函数的梯度选择合适的焦距评价函数和步长,并通过改变步长实现了从快速粗调到精确细调的过渡。实验表明,与传统的单一聚焦评价函数和单一步长的聚焦算法相比较,该算法在保证聚焦速度的情况下显著地提高了聚焦位置的精度。     

光切法形貌测量中光条中心的亚像素提取

为了在光切法三维形貌测量中快速准确提取光条的中心位置,保证测量精度,研究了一种光条中心的亚像素提取方法。将图像的灰度视为关于坐标的二维函数,在CCD拍摄的激光光条图像中求取灰度的偏导数和梯度,图像中每一点的梯度代表此点的灰度变化方向,将此方向简化为光条上的点的法线方向。对梯度的大小设定阈值并结合该点的灰度信息,提取图像灰度变化最激烈的部分,即光条饱和带的像素位置。对光条饱和带的每个像素,按照不重复计算的原则在梯度方向上和规定邻域内求灰度中心,得到精确到亚像素级的光条中心位置。此方法将光条曲线的法线方向简化为灰度的梯度方向,省去了求海赛矩阵以确定法向的步骤。实验证明,同等条件下,该方法的计算耗时为常规亚像素提取方法的10%。利用该算法进行实际测量,在200mm×200mm测量范围下,误差(3σ)为0.057mm,满足了测量对精确性和实时性的要求。     

离焦模糊图像清晰度评价函数的分析与改进

传统的基于梯度的图像清晰度评价函数(调焦函数)对噪声敏感,在实际应用中易引入过多的非边缘信息,影响系统的稳定性。本文基于光学成像系统离焦模型分析了系统离焦对图像清晰度的影响,并提出了一种改进的图像清晰度评价方法。该方法利用最大灰度差提取细节信息来评价图像清晰度;引入阈值区分边缘点和非边缘点来减小图像平坦区域对评价函数灵敏度的影响,同时有效抑制噪声的干扰。进行了仿真实验和实际测试并与传统的清晰度评价函数进行了比较。结果显示,提出的方法具有更好的灵敏度和抗噪性能,能够准确而稳定地评价离焦模糊图像的清晰度,可用于实际光学系统的自动调焦。     

一种有效的去除图像混合噪声的滤波算法

依据噪声特点以及图像的像素关联性,提出了一种有效的数字图像混合噪声滤波算法。该算法首先对图像像素进行检测,依据受脉冲噪声污染的图像像素与其周围邻域多数像素在亮度上具有显著差异的特点准确检测出脉冲噪声,然后以检测到的脉冲噪声为被处理像素,用其邻域内未被脉冲噪声污染的图像像素对其进行中值滤波,由于当前脉冲噪声点以及邻域脉冲噪声点均未参与滤波运算,从而较好地保护了图像的细节。对图像中的高斯噪声,提出了一种改进的加权均值滤波算法,该算法在定义相关度函数时,既考虑了像素的灰度相关性,又考虑了像素的位置相关性。实验结果表明:提出的混合噪声滤波算法不仅可以有效滤除图像中的混合噪声,而且还可以较好地保护图像的细节。     

基于E-SPCM的直线电机动子位置高精度测量方法研究

针对直线电机动子位置测量,引入一种基于扩展采样相位相关法(E-SPCM)的亚像素位移图像检测方法,以提高测量精度和抗干扰性。首先建立了直线电机位置检测系统,通过高速相机实时采集条纹图像序列;其次对条纹图像进行边缘特征提取,利用相位相关得到相邻条纹图像的互功率谱,即动子位置的整像素位移;进而对整像素邻域的互功率谱进行上采样相位相关计算,实现高精度的亚像素位移测量,进一步由系统标定得到实际位移值。对比传统相位相关算法,所采用的方法能够提高测量精度且有很好的噪声抑制性能,最后搭建了实验检测平台,验证了算法的有效性。     

Signal‐to‐noise ratio enhancement on SEM images using a cubic spline interpolation with Savitzky–Golay filters and weighted least squares error

A new technique based on cubic spline interpolation with Savitzky–Golay smoothing using weighted least squares error filter is enhanced for scanning electron microscope (SEM) images. A diversity of sample images is captured and the performance is found to be better when compared with the moving average and the standard median filters, with respect to eliminating noise. This technique can be implemented efficiently on real‐time SEM images, with all mandatory data for processing obtained from a single image. Noise in images, and particularly in SEM images, are undesirable. A new noise reduction technique, based on cubic spline interpolation with Savitzky–Golay and weighted least squares error method, is developed. We apply the combined technique to single image signal‐to‐noise ratio estimation and noise reduction for SEM imaging system. This autocorrelation‐based technique requires image details to be correlated over a few pixels, whereas the noise is assumed to be uncorrelated from pixel to pixel. The noise component is derived from the difference between the image autocorrelation at zero offset, and the estimation of the corresponding original autocorrelation. In the few test cases involving different images, the efficiency of the developed noise reduction filter is proved to be significantly better than those obtained from the other methods. Noise can be reduced efficiently with appropriate choice of scan rate from real‐time SEM images, without generating corruption or increasing scanning time.     

A microcomputer based digital imaging system for ion microanalysis

A microcomputer based digital imaging system was developed for a Cameca IMS-3f ion microscope permitting real-time digital acquisition of secondary ion images. Image signal-to-noise enhancement results from random noise reduction by real-time ensemble averaging and from a reduction of pattern noise in the charge injection device (CID) array by subtraction of blank frames. Acquired images comprise 244times248 pixel arrays with 8-bit intensity resolution. Images are displayed on a colour monitor in a grey scale or pseudo-colour using one of four programmable lookup tables. Image processing software permits off-line ion image enhancement and manipulation as well as multitechnique digital image correlations. System capability is illustrated by a biological example involving digital imaging studies of Al distribution in osteomalacic bone tissue, including correlative light microscopy and ion microanalysis.     

基于差商的油液监测铁谱图像自适应分割

针对油液监测中铁谱磨粒图像分割阈值难以选取的问题,本文提出一种基于差商的自适应铁谱图像分割算法。首先,将铁谱磨粒灰度图像转换成三维灰度直方图,并对其进行切片分析;然后,引入Newton插值多项式,将不同切片所得的频数作为切片灰度-频数曲线的插值点,基于差商构造第一类可接受函数和第二类可接受函数,结合实验数据确定两类误差,选取同时满足两类误差的最小灰度值作为分割阈值;最后,用本文方法对不同类型的磨粒图像以及添加高斯噪声和椒盐噪声后图像分别进行分割实验,并与经典的迭代阈值法、Otsu算法、最大熵法进行了比较。实验结果表明,本文方法受噪声干扰较小,误检率和漏检率整体优于其他3种算法。对分割所得的磨粒图像进行特征提取,并利用支持向量机进行识别,本文方法对3种故障磨粒识别准确率最高,达到82.86%,虽在运行时间上无明显优势,但综合性能最优,能满足油液监测过程中铁谱图像自适应分割的需求。     

一种改进的FCM图像分割算法

提出了一种基于高维特征矢量与金字塔结构的模糊聚类图像分割方法。该方法考虑了图像像点的灰度信息和其邻域空间相关信息,给出了三维特征及多分辨率分析的聚类分割方法。实验结果表明,在图像受高斯噪声干扰的情况下,该方法不仅运行速度较快,而且分割效果更好。     

Inherent bias in correlation averaged images

The correlation averaging algorithm frequently used to enhance micrographs of repeating structures contains an inherent bias that favours images with larger pixel values or positive noise levels. This bias not only skews the composite image toward higher pixel values, but also distorts the image by increasing the value of high-valued pixels more than that of low-valued pixels. These errors are especially important in scanning probe microscopy images where the pixel value reflects a distinct height. A similar algorithm that uses a structure function in place of the correlation function eliminates this bias.     

CCD像素响应不均匀的校正方法研究

在机器视觉的应用中,人们习惯认为CCD输出的像素灰度值反映了目标的图像信息,因此大多数情况中忽略了灰度值的标定,但由于CCD中存在的暗电流和像素的不均匀性,其上的每个像素在同一辐照度下形成的灰度值,并不相同,所以在光学精密测量过程中,需要对CCD各像素的灰度值进行校正,使其在相同辐照度下,输出确定的灰度值,以便反映目标的正确信息。文中通过对造成CCD像素灰度不均匀的原因详细分析,提出了一种基于最小二乘法的CCD非均匀的校正算法,该算法通过求取期望灰度值和辐照度的关系参数,推导出了像素的补正参数。仿真表明,该方法能有效地校正CCD像素的非均匀性。     

Multidirectional focus measure for accurate three-dimensional shape recovery of microscopic objects

Shape from focus (SFF) is a technique to recover the shape of an object from multiple images taken at various focus settings. Most of conventional SFF techniques compute focus value of a pixel by applying one of focus measure operators on neighboring pixels on the same image frame. However, in the optics with limited depth of field, neighboring pixels of an image have different degree of focus for curved objects, thus the computed focus value does not reflect the accurate focus level of the pixel. Ideally, an accurate focus value of a pixel needs to be measured from the neighboring pixels lying on tangential plane of the pixel in image space. In this article, a tangential plane on each pixel location (i, j) in image sensor is searched by selecting one of five candidate planes based on the assumption that the maximum variance of focus values along the optical axis is achieved from the neighborhood lying on tangential plane of the pixel (i, j). Then, a focus measure operator is applied on neighboring pixels lying on the searched plane. The experimental results on both the synthetic and real microscopic objects show the proposed method produces more accurate three-dimensional shape in comparison to conventional SFF method that applies focus measures on original image planes.     

PCA-based method for 3D shape recovery of microscopic objects from image focus using discrete cosine transform

This article introduces a new algorithm for shape from focus (SFF) based on discrete cosine transform (DCT) and principal component analysis (PCA). DCT is applied on a small 3D neighborhood for each pixel in the image volume. Instead of summing all focus values in a window, AC parts of DCT are collected and then PCA is applied to transform this data into eigenspace. The first feature, containing maximum variation is employed to compute the depth. DCT and PCA are computationally intensive; however, the reduced data elements and algorithm iterations have made the new approach competitive and efficient. The performance of the proposed approach is compared with other methods by conducting experiments using image sequences of a synthetic and two microscopic objects. The evaluation is gauged on the basis of unimodality, monotonicity, and resolution of the focus curve. Two other global statistical metrics, root mean square error (RMSE) and correlation have also been applied for synthetic image sequence. Besides, noise sensitivity and computational complexity are also compared with other algorithms. Experimental results demonstrate the effectiveness and the robustness of the new method.