面向微操作的显微视觉系统自动聚焦优化算法

研究了面向微操作的显微视觉系统自动聚焦评价函数和聚焦控制策略。首先,分析显微图像特点并做预处理;接着引入像素相关性指标,并结合梯度函数形成一种新的图形清晰度评价函数,改善了函数的灵敏度和抗噪性;最后对传统爬山算法进行改进,在粗调阶段以大步长搜索并用曲线拟合的方法快速定位到峰值点附近,精调阶段以小步长搜索到评价函数值下降点即可准确定位到焦平面,该算法避免了复杂的阈值设定问题,与传统爬山法相比,在一定程度上提高了聚焦速度,并大幅提高了聚焦成功率。     

显微视觉系统中自动聚焦算法的研究

针对自动聚焦过程中的聚焦评价函数、聚焦窗口、搜索算法进行了研究。首先改进了Robert函数增加了聚焦评价函数的陡峭度;其次针对固定聚焦窗口无法准确找到聚焦物体的缺点,提出了一种动态选择聚焦窗口的方法,该方法将图像分块,利用不同子块的梯度变化程度区分物体和背景,该方法更具适应性;进而提出了大步长和小步长相结合的爬山搜索算法,经过改进后的爬山搜索算法能更准确地找到焦平面;最后通过自行研发的显微视觉系统验证了所提自动聚焦方案的有效性。     

基于边缘特征提取的图像清晰度评价函数

图像清晰度评价函数是清晰度法自动调焦技术中用来反馈离焦状态的一个度量工具。调焦系统可以通过对成像清晰度的评价来自动搜索到成像最清晰位置。图像中边缘梯度能量大小反映了一幅图像的清晰状况,根据图像最清晰时图像中边缘梯度最大的特点,设计了一系列基于边缘梯度算子的清晰度评价函数。首先从理论上论证了这一系列函数与其它函数相比更具有鲁棒性、直观性的优点,然后通过实验验证,这一系列函数都符合了调焦函数中单峰性、抗噪性和无偏性的使用要求。     

一种改进的自动聚焦搜索算法及其实现

自动聚焦的过程就是镜头按照自动聚焦搜索算法来回搜索聚焦评价函数求最大值的过程。传统的爬山搜索算法会受到聚焦评价函数局部极值的干扰而不能准确聚焦,并且在大幅离焦状态下近聚焦缓慢。详细介绍了一种改进的自动聚焦搜索算法的原理和实现方法,它能有效地排除这种干扰,减少大幅离焦状态下近聚焦的时间,使系统可靠的聚焦。     

数字图像清晰度评价函数的研究与改进

通过分析常见的图像清晰度评价函数,针对自动对焦系统中图像清晰度评价问题,提出了一种基于聚焦窗口的改进梯度评价函数算法。改进后的算法具有计算量小、实时性好、评价曲线单峰性好、灵敏度高、聚焦检测效率高等特点,可以更好地满足自动对焦系统对图像清晰度评价的要求。     

基于小波分析的显微视觉景深扩展

光学显微视觉系统的主要特点在于景深短,难以获取反映显微场景的全面信息。为解决该问题,提出基于小波分析的光学显微视觉系统景深扩展策略,分为局部清晰图像获取和多聚焦图像融合两个阶段：首先,以定义的小波系数活性水平为依据,构造了新型清晰度评价函数和聚焦曲线全局极值搜索策略来实现快速自动聚焦、获取局部显微场景信息的目的;然后,为实现多局部聚焦显微图像的融合,设计了小波系数活性水平选择型融合规则来融合获取的多个局部显微场景信息。实验表明,提出方法可有效扩展光学显微镜的景深。     

数字自动对焦中的搜索算法研究

设计了综合的对焦搜索算法。利用对焦范围上下限、预判定搜索方向、评价函数值连续下降两次才认为越过对焦曲线峰值与设定下降阈值,改进了爬山搜索法。在对焦镜偏离光学对焦范围情况下,利用对焦范围上下限与预判定搜索方向,改进了穷举搜索法;对完焦后再校验场景是否正确,光照低于设定阈值时则关闭搜索,可提高抗干扰能力;利用成像曲线,变倍镜移动时能快速地对焦。实验表明:采用综合搜索算法能够实现准确、快速的对焦,避免搜索陷入局部峰值,具有良好的抗干扰与稳定性。     

显微视觉系统对柱状微零件自动聚焦技术研究

针对显微视觉中的柱状物体图像的清晰度问题,提出了一种实时检测图像特征并跟踪特征区域进行自动聚焦的方法。该聚焦算法包括聚焦评价函数、聚焦搜索算法和聚焦区域选择。该聚焦搜索算法实现了显微视觉系统下快速准确的聚焦,克服了爬山搜索算法的缺点,有效避免搜索结果陷入局部极大值。显微视觉中对聚焦区域的选择尤为重要,以图像特征区域作为聚焦区域,实时检测该特征区域进行聚焦,实现了在景深小于柱状物体半径的情形下对柱状物体边缘的精确聚焦。粗聚焦后电机位置的标准差为205μm,精聚焦后电机位置的标准差为37μm。实验结果表明,该自动聚焦方法能够满足微装配系统对显微视觉的聚焦需求。     

基于高清网络摄像机的自动聚焦算法

针对传统的聚焦评价函数运算量大、峰值搜索算法易受干扰且视频质量反复变化的问题,实现一种较传统聚焦评价函数与爬山算法相结合的聚焦算法更好的自动聚焦算法.采用TI公司的TMS320DM368处理器自带的自动聚焦(AF)引擎作为硬件实现的聚焦评价函数;采用的峰值搜索算法将聚焦曲线分为聚焦区和散焦区,在散焦区使用大步长的GM(1,1)模型预测搜索方向,在聚焦区使用小步长的爬山算法搜索峰值.实验结果表明,该算法拥有更好地实时性与准确性,适用于高清网络摄像机的自动聚焦.     

TFT-LCD面板反射的能量对光斑图像的影响

针对基于激光三角测距原理设计的显微镜自动对焦系统中,光斑在TFT-LCD面板上表面或者下表面聚焦时的鬼影以及打在金属丝上的光斑畸变问题,提出在焦平面附近调节相机快门和增益以消除鬼影,并根据相机快门时间动态变换光斑中心的求解方法.分析了光斑鬼影产生的原因和消除方法以及当光斑图像不同程度打在金属丝上时,导致光斑图像局部明显增强的特性,给出了求解光斑中心的方法.实验结果表明,光斑中心位置和离焦量线性拟合R的平方值为0.991,减少了激光三角测距法的非线性误差,实现了非接触快速对焦.     

Edge and Depth from Focus

This paper proposes a novel method to obtain the reliable edge and depth information by integrating a set of multi-focus images, i.e., a sequence of images taken by systematically varying a camera parameter focus. In previous work on depth measurement using focusing or defocusing, the accuracy depends upon the size and location of local windows where the amount of blur is measured. In contrast, no windowing is needed in our method; the blur is evaluated from the intensity change along corresponding pixels in the multi-focus images. Such a blur analysis enables us not only to detect the edge points without using spatial differentiation but also to estimate the depth with high accuracy. In addition, the analysis result is stable because the proposed method involves integral computations such as summation and least-square model fitting. This paper first discusses the fundamental properties of multi-focus images based on a step edge model. Then, two algorithms are presented: edge detection using an accumulated defocus image which represents the spatial distribution of blur, and depth estimation using a spatio-focal image which represents the intensity distribution along focus axis. The experimental results demonstrate that the highly precise measurement has been achieved: 0.5 pixel position fluctuation in edge detection and 0.2% error at 2.4 m in depth estimation.     

基于频谱预处理与改进霍夫变换的离焦模糊盲复原算法

为了提高离焦模糊图像复原清晰度,提出一种基于频谱预处理与改进霍夫变换的 离焦模糊盲复原算法。首先改进模糊图像频谱预处理策略,降低了噪声对零点暗圆检测的影响。 然后改进霍夫变换圆检测算法,在降低算法复杂度的同时,增强了模糊半径估计的准确性。最 后利用混合特性正则化复原图像模型对模糊图像进行迭代复原,使复原图像的边缘细节更加清 晰。实验结果表明,提出的模糊半径估计方法较其他方法平均误差更小,改进的频谱预处理策 略更有利于零点暗圆检测,改进的霍夫变换圆检测算法模糊半径估计精度更高,所提算法对已 知相机失焦的小型无人机拍摄的离焦模糊图像具有更好的复原效果。针对离焦模糊图像复原, 通过理论分析和实验验证了改进的模糊半径估计方法的鲁棒性强,所提算法的复原效果较好。     

聚焦性检测与彩色信息引导的光场图像深度提取

目的 光场相机可以通过一次拍摄,获取立体空间中的4D光场数据,渲染出焦点堆栈图像,然后采用聚焦性检测函数从中提取深度信息。然而,不同聚焦性检测函数响应特性不同,不能适应于所有的场景,且现有多数方法提取的深度信息散焦误差较大,鲁棒性较差。针对该问题,提出一种新的基于光场聚焦性检测函数的深度提取方法,获取高精度的深度信息。方法 设计加窗的梯度均方差聚焦性检测函数,提取焦点堆栈图像中的深度信息;利用全聚焦彩色图像和散焦函数标记图像中的散焦区域,使用邻域搜索算法修正散焦误差。最后利用马尔可夫随机场(MRF)将修正后的拉普拉斯算子提取的深度图与梯度均方差函数得到的深度图融合,得到高精确度的深度图像。结果 在Lytro数据集和自行采集的测试数据上,相比于其他先进的算法,本文方法提取的深度信息噪声较少。精确度平均提高约9.29%,均方误差平均降低约0.056。结论 本文方法提取的深度信息颗粒噪声更少;结合彩色信息引导,有效修正了散焦误差。对于平滑区域较多的场景,深度提取效果较好。     

A divide and conquer deformable contour method with a model based searching algorithm

A divide and conquer deformable contour method is presented with an initial inside closed contour being divided into arbitrary segments, and these segments are allowed to deform separately preserving the segments' connectivity. A maximum area threshold, A/sub max/, is used to stop these outward contour segments' marching. Clear and blur contour points are then identified to partition the whole contour into clear and blur segments. A bi-directional searching method is then recursively applied to each blur segment including a search for contour-within-contour segment to reach a final close contour. Further improvements are provided by a model based searching algorithm. It is a two-step process with step 1 being a linked contour model matching operation where landmarks are extracted, and step 2 being a posteriori probability model matching and correction operation where large error segments are fine tuned to obtain the final results. The experiments include ultrasound images of pig heart, MRI brain images, MRI knee images having complex shapes with or without gaps, and inhomogeneous interior and contour region brightness distributions. These experiments have shown that the method has the capability of moving a contour into the neighboring region of the desired boundary by overcoming inhomogeneous interior, and by adapting each contour segment searching operation to different local difficulties, through a contour partition and repartition scheme in searching for a final solution.     

Performance Analysis of Maximum Likelihood Estimator for Recovery of Depth from Defocused Images and Optimal Selection of Camera Parameters

The recovery of depth from defocused images involves calculating the depth of various points in a scene by modeling the effect that the focal parameters of the camera have on images acquired with a small depth of field. In the existing methods on depth from defocus (DFD), two defocused images of a scene are obtained by capturing the scene with different sets of camera parameters. Although the DFD technique is computationally simple, the accuracy is somewhat limited compared to the stereo algorithms. Further, an arbitrary selection of the camera settings can result in observed images whose relative blurring is insufficient to yield a good estimate of the depth. In this paper, we address the DFD problem as a maximum likelihood (ML) based blur identification problem. We carry out performance analysis of the ML estimator and study the effect of the degree of relative blurring on the accuracy of the estimate of the depth. We propose a criterion for optimal selection of camera parameters to obtain an improved estimate of the depth. The optimality criterion is based on the Cramer-Rao bound of the variance of the error in the estimate of blur. A number of simulations as well as experimental results on real images are presented to substantiate our claims.     

融合IMU去除运动模糊的改进光流匹配算法

为进一步提高视觉SLAM中的光流匹配精度和速度,提出一种融合惯性测量单元（inertial measurement unit,IMU）去除运动模糊的改进光流匹配算法。该算法首先利用IMU运动信息计算的点扩散函数去除运动模糊,提高特征点匹配率;其次在LK（Lucas-Kanade）光流的基础上引入梯度误差,并使用图像梯度L1范数作为正则项模拟稀疏噪声,构建代价函数;然后利用IMU预测特征点位置作为该算法初始值,并加入BB（Barzilar-Borwein）步长改进原有的高斯牛顿算法,提高计算速度。实验表明,通过两帧之间比较,该算法的效率和精度均优于LK光流法;然后将该算法集成到VINS-Mono框架,在数据集EuRoC上结果显示,该算法提高了原有框架的定位精度和鲁棒性。     

亮度—梯度联合约束的车牌图像超分辨率重建

目的 受成像距离、光照条件、动态模糊等因素影响,监控系统拍摄的车牌图像往往并不具备较高的可辨识度。为改善成像质量,提升对车牌的识别能力,提出一种基于亮度与梯度联合约束的车牌图像超分辨率重建方法。方法 首先充分结合亮度约束和梯度约束的优势,实现对运动位移和模糊函数的精确估计;为抑制重建图像中的噪声与伪影,基于车牌图像的文字化特征,进一步确定了亮度与梯度联合约束的图像先验模型。结果 为验证该方法的有效性,利用监控系统获得4组车牌图像,分别进行模拟和真实的超分辨率重建实验。在模拟实验中将联合约束图像先验重建结果与拉普拉斯、Huber-Markov（HMRF）以及总变分（TV）先验的处理结果进行对比,联合约束先验对车牌纹理信息的恢复效果优于其他3种常见图像先验;同时,在模拟和真实实验中,将本文算法与双三次插值、传统最大后验概率、非线性扩散正则化和自适应范数正则化方法的超分辨率重建结果进行比较,模拟实验的结果表明,在不添加噪声情况下,该算法峰值信噪比（PSNR）和结构相似性（SSIM）指标分别为35.326 dB和0.958,优于其他4种算法;该算法在真实实验中,能够有效增强车牌图像纹理信息,获得较优的视觉效果,通过对重建车牌图像的字符识别精度比较,本文算法重建结果的识别精度远高于其他3种算法,平均字符差距为1.3。结论 模拟和真实图像序列的实验结果证明,基于亮度—梯度联合约束的超分辨率重建方法,能够降低运动和模糊等参数的估计误差,有效减少图像中存在的模糊和噪声,提高车牌的识别精度。该算法广泛适用于因光照变化、相对运动等因素影响下的低质量车牌图像超分辨率重建。     

Multi-focus image fusion using Content Adaptive Blurring

Multi-focus image fusion has emerged as an important research area in information fusion. It aims at increasing the depth-of-field by extracting focused regions from multiple partially focused images, and merging them together to produce a composite image in which all objects are in focus. In this paper, a novel multi-focus image fusion algorithm is presented in which the task of detecting the focused regions is achieved using a Content Adaptive Blurring (CAB) algorithm. The proposed algorithm induces non-uniform blur in a multi-focus image depending on its underlying content. In particular, it analyzes the local image quality in a neighborhood and determines if the blur should be induced or not without losing image quality. In CAB, pixels belonging to the blur regions receive little or no blur at all, whereas the focused regions receive significant blur. Absolute difference of the original image and the CAB-blurred image yields initial segmentation map, which is further refined using morphological operators and graph-cut techniques to improve the segmentation accuracy. Quantitative and qualitative evaluations and comparisons with current state-of-the-art on two publicly available datasets demonstrate the strength of the proposed algorithm.     

A new sense for depth of field

This paper examines a novel source of depth information: focal gradients resulting from the limited depth of field inherent in most optical systems. Previously, autofocus schemes have used depth of field to measured depth by searching for the lens setting that gives the best focus, repeating this search separately for each image point. This search is unnecessary, for there is a smooth gradient of focus as a function of depth. By measuring the amount of defocus, therefore, we can estimate depth simultaneously at all points, using only one or two images. It is proved that this source of information can be used to make reliable depth maps of useful accuracy with relatively minimal computation. Experiments with realistic imagery show that measurement of these optical gradients can provide depth information roughly comparable to stereo disparity or motion parallax, while avoiding image-to-image matching problems.     

一种离焦模糊图像客观检测的新方法

为了更精确地进行离焦模糊图像检测,提出了一种针对一幅图的离焦模糊图像客观检测的新方法,其核心思想是由线扩展函数(LSF)得到离焦模糊图像的点扩展函数(PSF)。该方法首先假定图像中至少能检测到一条明显边缘,然后由此边缘构造LSF。由于作用于空间域,无需复杂的傅里叶变换或迭代运算,因此该方法速度很快。此外,为使检测方法更具普遍性,还提出了离焦模糊检测的一般准则,这种准则适用于所有图像而不依赖于图像的内容。实验结果验证了该方法的精确性和有效性。