Bilateral prediction and intersection calculation autofocus method for automated microscopy

In this paper, a bilateral prediction and intersection calculation autofocus method for automated microscopy, which obtains the in‐focus position by calculating the intersection of the predicted left and right focus measure curves located respectively in the left and right sides of the peak position of the focus measure curve, is proposed and performed. According to the autofocus method, the area including the peak position of the focus measure curve and its left and right neighbourhoods should be determined firstly, and the left and right neighbourhoods are considered as the left and right sampling areas. The left and right focus measure curves are predicted by appropriate predicting models according to the two sample sequences, which comprise the focus values by evaluating the sampled images in the left sampling area and right sampling area, respectively and their corresponding sampling positions. The intersection of the predicted left and right focus measure curves is calculated and can be considered as the in‐focus position. The autofocus can be realized by moving the focus plane of the microscope to the intersection of the predicted left and right focus measure curves. The proposed bilateral prediction and intersection calculation autofocus method is experimentally verified in an automated light microscopy for implementing microassembly and micromanipulation. The theoretical analyses have shown that the proposed bilateral prediction and intersection calculation autofocus method can not only effectively avoid the principle error caused by assuming the symmetrical focus measure curve in the autofocus methods based on curve fitting, but also eliminate the possible waver search near the peak position in the modified fast climbing servo method. The experimental results have shown that the proposed bilateral prediction and intersection calculation autofocus method possesses the merits as follows: (1) the focusing accuracy is high and slightly affected by the sampling step size and (2) the focusing speed is higher than those of the 7‐point hill‐climbing search method and the quadratic curve fitting method with a determinate focusing accuracy.     

图像清晰度评价函数的研究

为了实现更为高效、准确的光纤端面自动对焦,对现有图像的清晰度评价函数进行了研究,并在此基础上,提出了一种新的计算简便、抗噪性好的清晰度评价函数。采用光纤几何参数测试仪获得光纤端面清晰成像面前后等间隔的24帧光纤端面图像,利用不同的图像清晰度评价函数对这24帧图像进行计算,得到相应的调焦曲线并分析其单峰性、单调性和抗噪性。结果显示,所提的新评价函数具有更好的表现,对自动对焦技术具有一定的参考价值。     

A texture-analysis-based design method for self-adaptive focus criterion function

Autofocusing (AF) criterion functions are critical to the performance of a passive autofocusing system in automatic video microscopy. Most of the autofocusing criterion functions proposed are dependent on the imaging system and image captured by the objective being focused or ranged. This dependence destabilizes the performance of the system when the criterion functions are applied to objectives with different characteristics. In this paper, a new design method for autofocusing criterion functions is introduced. This method enables the system to have the ability to tell the texture directional information of the objective. Based on this information, the optimal focus criterion function specific to one texture direction is designed, voiding blindly using autofocusing functions which cannot perform well when applied to the certain surface and can even lead to failure of the whole process. In this way, we improved the self-adaptability, robustness, reliability and focusing accuracy of the algorithm. First, the grey-level co-occurrence matrices of real-time images are calculated in four directions. Next, the contrast values of the four matrices are computed and then compared. The result reflects the directional information of the measured objective surfaces. Finally, with the directional information, an adaptive criterion function is constructed. To demonstrate the effectiveness of the new focus algorithm, we conducted experiments on different texture surfaces and compared the results with those obtained by existing algorithms. The proposed algorithm excellently performs with different measured objectives.     

嵌入式自动聚焦摄像模组控制系统的设计

针对手机等移动终端设备中的摄像模组,提出了一种嵌入式自动聚焦控制系统。该系统以AD5820作为聚焦电机的驱动IC,以OV5642作为图像传感器,利用68013单片机进行数据处理并控制驱动IC调整镜头的位置,完成准确自动聚焦。系统通过控制音圈电机带动镜头行程变位,获取一系列图像;计算每幅图像清晰度评价值构成清晰度评价曲线;采用梯度函数作为图像清晰度评价标准;并用全程搜索的方式找到图像清晰度最大值时镜头的位置,从而达到聚焦的目的。实验验证显示,系统的聚焦分辨率能达到5～10μm,响应速度小于70ms。自动聚焦实物拍摄图像清晰,能够很好地满足摄像模组自动聚焦的需求。     

图像调焦过程的清晰度评价函数研究

自动调焦是一类建立在搜索算法上的调焦方法,它通过计算机编程对不同对焦位置所成像的清晰度进行评价,利用正确对焦时图像最清晰这个特征找到正确的对焦位置。判断图像对焦与否是通过图像清晰度对焦评价函数/调焦状态评价函数(调焦判定函数)来衡量的。利用每一个调焦判定函数对包含了一组模糊和清晰图像的序列图像进行处理,可以得到对应于相应判定函数的调焦曲线,利用曲线可以非常直观地分析判定函数的性能。结果表明,灰度梯度函数具有比较稳定的调焦特性。     

基于动态因子自适应搜索算法的自动调焦研究

为提高自动调焦的性能,采取动态因子自适应搜索算法。首先,建立分辨率逐步递减的调焦窗口,以减少非聚焦区域对调焦评价函数计算的影响;其次,通过动态调焦因子方法修正目标变化对清晰度评价函数的影响;最后,通过适应变步长爬山算法结合清晰度评价函数搜索聚焦点,提高搜索速度和调焦精度。试验仿真显示,本文提出的算法获得的图像清晰,其性能受噪声影响小,可满足自动调焦中对稳定性、实时性和清晰度的要求。     

An in-depth study of tool wear monitoring technique based on image segmentation and texture analysis

We present a new micro-vision system for tool wear monitoring, which is essential for intelligent manufacturing. The tool wear area is divided into regions by a watershed transform, then subjected to automatic focusing and segmentation. The individual pixel gray values in each region are then replaced with the corresponding regional mean gray value. A hill climbing algorithm based on the sum modified laplacian (SML) focusing evaluation function is used to search the focal plane. In addition, we implement an adaptive Markov Random Field (MRF) algorithm to segment each region of tool wear. For our MRF model, the connection parameter value is adaptively determined by the connection degree between regions, which improves image acquisition of more integral tool wear areas. Our findings suggest that automatic focusing and segmentation of the tool wear area by region (within the tool wear area) enhance accuracy and robustness, and allow for real time acquisition of tool wear images. We also implement a complementary tool wear assessment procedure based on the surface texture of the workpiece. The optimal texture analysis window is determined using the entropy metric – a texture feature generated using a Gray Level Co-occurrence Matrix (GLCM). In the best texture analysis window, entropy remains monotonic as tool wear increases, demonstrating that entropy can be used effectively to monitor tool wear. Information from combined measurements of tool wear and workpiece texture can reliably be used to monitor tool wear conditions and improve monitoring success rates.     

基于加权邻域相关性的显微镜自动聚焦函数

目的：提出了一种基于加权邻域相关性的显微镜自动聚焦函数,并在研究显微镜聚焦原理及成像过程的基础上,分析了显微镜图像中像素邻域灰度相关性及像散现象对聚焦评价的影响。方法：首先,分别计算每幅显微镜序列图像中各像素与其四邻域像素的灰度相关性。然后,计算基于此相关性加权平均值的二次多项式聚焦函数,其中权值根据对应像素与显微镜视场中心的距离来确定。最后,选取该函数值最大的图像为聚焦图像。结果：实验结果表明,与经典的聚焦函数如方差函数、绝对梯度函数、Roberts梯度函数及Tenengrad函数相比,本文方法的聚焦灵敏度因子提高了0.3185~0.3268,噪声环境下聚焦的平均正确率提高了0~40%。结论：该方法能够准确地评价图像聚焦的程度,并具有较高的灵敏度和较强的抗噪性。     

推扫式高光谱相机自动调焦方法(中国光学工程学会推荐稿件)

自动调焦方法已广泛得应用到各种光学仪器中。为了解决推扫式高光谱相机自动调焦问题,提出了一种利用基于四元数小波变换的光谱质量评价函数估计相机焦距的方法。该方法首先将推扫线上单行光谱数据顺序排列成一个二维矩阵,利用四元数小波变换对光谱像元矩阵进行分解得到低频和高频四个子带。通过对低频和高频的幅值和相位信息构造光谱质量评价函数,指导相机自动调焦。调焦过程中,利用自动调焦机构按一定步长旋转镜头,计算不同离焦状态下的光谱质量指标值,拟合光谱质量指标值与镜头伸缩量之间的高斯分布函数,最终估算最佳调焦量。实验表明,提出的光谱质量评价函数灵敏度高、准确度强。该调焦方法仅利用单行光谱数据进行调焦,调焦准确。     

一种改进型灰度差分聚焦算法研究与实现

为解决显微图像采集中聚焦的准确性和快速性,设计并实现了一种包括图像采集端、图像处理算法实现模块、OSD菜单实现模块、系统控制模块和HDMI输出模块等硬件采集系统。结合实际应用和现有的图像处理算法,提出了适合数字显微图像的图像预处理自动聚焦算法,在灰度差分绝对值之和函数的基础上增加了对周围点的判断,提高了算法的精确度。实验结果表明,通过对切片图像在显微镜物镜10倍、40倍、100倍下采集到的离、聚焦图像以及聚焦曲线呈现单峰性,无偏性等特点,能实现良好的聚焦能力。     

基于径向基函数网络的图像三维恢复技术在雕刻加工中的应用

基于图像的三维恢复技术的目的是采用单幅图像中物体表面的明暗变化来恢复其表面各点的相对高度,从而对三维物体进行曲面恢复。提出了一种基于径向基函数网络模型进行图像三维恢复的新方法,建立了径向基函数的网络模型,并构造出一个曲面公式, 其中输出对两个输入变量的偏导数满足反射函数方程,反射函数方程中的灰度值已知,利用反射函数与灰度值之间的误差作为约束条件调节网络权因子、径向基函数中心和宽度,使得构造的曲面中每一点都满足反射函数方程,从而拟合整个曲面。该方法无需光滑约束和积分约束条件,求出的解是一个连续解。通过不同方法对合成图像圆球和花瓶的实验表明,该算法恢复的曲面的高度点最大误差精度提高1倍到4倍,平均误差精度提高5倍到20倍,恢复的曲面具有良好的连续性和光滑性,它不仅可以恢复图像中各网络点的高度,而且可自动内插网络点之间任意点,适合于任意反射模型图像的曲面反求,恢复出的曲面便于进行后置处理生成刀具路径,并进行雕刻加工。     

一种变物距的自动对焦方法

针对像距无法改变的特定环境,提出了一种变物距的自动对焦方法。首先通过设置标志矩形,采用SUSAN算法中的角点获取算法,取得对焦窗口;其次建立对焦窗口转换函数,使不同物距下的对焦窗口包括相同的成像信息;最后使用能量梯度对焦评价函数,取得最佳对焦位置。实验表明,该系统适用于像距无法改变的环境,并且具有良好的自动对焦精度、速度以及鲁棒性。     

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。     

基于非特定图像的红外成像系统调制 传递函数自动测量方法

机载红外成像系统的性能评估对于战机任务执行效能具有重要意义。 调制传递函数(MTF)是衡量红外成像系统性能 的典型指标。 现阶段 MTF 的测量依赖于特定的图像特征与人工操作,难以实现实时与批量评估。 为此,提出了一种基于非特 定图像的 MTF 自动测量方法,以倾斜刃边法为基础,通过图像刃边检测、目标区域提取以及改进的刃边计算方法,从不含有特 定景物特征的实拍图像中自动获得 MTF 点值,并构造红外成像系统等效物理模型以实现 MTF 曲线的拟合,进一步滤除随机误 差。 最后,以某型号无人机红外成像系统作为验证对象,将实验室测得的 MTF 与基于非特定实拍图像计算的 MTF 进行对比, 两者的绝对误差在 0. 06 以内,证明了方法的可行性与有效性。 该自动测量方法能够较为准确且实时地得到系统的 MTF,为不 便进行地面实验的红外成像系统提供了一种新的性能测试思路。     

一种复合的自动对焦方法在影像测量仪中的应用

介绍了三种常用的自动对焦评价函数——方差评价函数、梯度评价函数、频谱评价函数,并对后两种评价函数进行了优化。对比分析了三种评价函数在评价的灵敏度、稳定性和计算速度上的优缺点,在此基础上提出了一种三阶段复合的自动对焦方法,该方法在影像测量仪上表现出良好的对焦特性,可以在保证对焦精度的同时实现快速对焦。     

广角望远镜实时自动调焦的清晰度评价方法

SVOM天文卫星附属的地基广角相机阵(GWAC)含有36个广角望远镜,其短时标的高精度自动观测建立在实时自动调焦的基础上,本文将对广角望远镜实时自动调焦的图像清晰度评价方法进行研究和实现。文章首先比较研究了常用望远镜图像清晰度评价方法的原理及在GWAC系统上的适用性,得出基于星像能量集中度的两种方法:星像50%能量半径、PSF的FWHM值适用于GWAC系统。区别于常用天文软件包IRAF费时的算法,本文提出了基于点源强度分布进行PSF拟合计算FWHM的方法,并进一步探究了诸如拟合模型、选星标准、定心精度、拟合半径、插值方法、插值间隔、FWHM后处理等关键方法参数对FWHM计算精度与速度的影响。最终得到一套适用于GWAC实时自动调焦的清晰度评价方法,并用C++编程实现。本文中方法 FWHM计算误差为0.046pixel,精度与IRAF相当,计算焦点位置一致;单图(挑选后约300颗星)计算时间为0.67s,约为IRAF计算时间的1/20,满足GWAC系统自动调焦的精度与实时性需求。研究结果在GWAC系统中得到应用,并可为其他自动化望远镜观测系统提供参考。     

角膜地形图仪中调焦函数的选取及搜索方法

选取适合角膜地形图仪的调焦函数并设计搜索方法。基于角膜地形图仪的图像拍摄要求及调焦系统原理,采用八种调焦评价函数对实验图像进行处理,对比分析不同调焦函数的单峰性、一致性、灵敏度、计算速度等指标。结果显示:除了Variance函数的单峰性较差,其他七种函数都有良好的单峰性、一致性和计算速度,而Laplacian函数灵敏度最高,有效测量范围小,适合精确调焦;NRSS函数灵敏度低,有效测量范围大,适合粗调焦。基于所选取的调焦评价函数,针对角膜地形图仪调焦系统,设计了一种基于阈值判别条件的搜索方法,确定其调焦阈值,并通过实验验证该方案的有效性。     

A self‐adaptive and nonmechanical motion autofocusing system for optical microscopes

For the design of a passive autofocusing (AF) system for optical microscopes, many time‐consuming and tedious experiments have been performed to determine and design a better focus criterion function, owing to the sample‐dependence of this function. To accelerate the development of the AF systems in optical microscopes and to increase AF speed as well as maintain the AF accuracy, this study proposes a self‐adaptive and nonmechanical motion AF system. The presented AF system does not require the selection and design of a focus criterion function when it is developed. Instead, the system can automatically determine a better focus criterion function for an observed sample by analyzing the texture features of the sample and subsequently perform an AF procedure to bring the sample into focus in the objective of an optical microscope. In addition, to increase the AF speed, the Z axis scanning of the mechanical motion of the sample or the objective is replaced by focusing scanning performed by a liquid lens, which is driven by an electrical current and does not involve mechanical motion. Experiments show that the reproducibility of the results obtained with the proposed self‐adaptive and nonmechanical motion AF system is better than that provided by that of traditional AF systems, and that the AF speed is 10 times faster than that of traditional AF systems. Also, the self‐adaptive function increased the speed of AF process by an average of 10.5% than Laplacian and Tenegrad functions.     

基于显微视觉的MEMS微装配系统研究

提出一种基于显微视觉伺服控制的MEMS器件微装配系统设计方案,描述了系统样机及关键技术模块的研制方案。以微型光谱分析仪极板粘接为示范应用开展了实验研究,表明该系统适用于典型MEMS芯片的微对准和封装研究。     

SEM自动聚焦系统设计

扫描电子显微镜（SEM）是用于观测微观世界的工具。扫描图像系统的聚焦好坏直接影响了观测的结果。反复的人为操作不仅使操作者疲惫,更会带来主观的误差。本系统依靠FEI公司提供的接口函数（API）采集样品的图像,利用DCT变换对图像进行频域性质分析,设计出图像清晰度评价函数,并且使用爬山算法搜索出最佳聚焦物镜电流,成功设计出高精度、鲁棒性高的自动调焦软件,实现了调焦的自动化。