快速自动聚焦算法在影像精密测高中的应用

快速、有效地实现聚焦是图像法测高的焦点,选择合适的焦点评价函数和设计高效的图像自动聚焦算法是其中的关键问题。在比较研究图像评价函数的基础上,选择灰度方差作为焦点评价函数,研究了一种粗略试探和微步距精确搜索相结合的自动聚焦算法,并分析了影像聚焦精度和速度的影响因素。该聚焦算法应用于自动影像测量仪,通过对各种材质的表面在变倍镜头的不同倍率下做聚焦和测高试验,获得结果并进行了详细的比较分析。结果表明,该算法稳定、可靠、高效,测量结果精确。     

一种新的基于清晰度的多聚焦图像融合规则

针对多聚焦图像,融合过程采用计算单个象素的清晰度时考虑以该象素为中心的邻域,通过对两幅输入图像中的对应部分进行加权组合来形成融合图像。该文提出了一种新的基于清晰度的图像融合规则,即采用保形有理三次插值样条函数来构造权值进行融合,试验结果表明该文提出的方法对于严格配准的多聚焦图像的融合效果比基于多分辨率的小波分解的融合算法好。     

基于相对清晰度的多焦距图像融合算法

针对现有图像域融合算法难以合理度量区域清晰度,导致融合结果出现"振铃"现象的问题,提出一种基于图像区域相对清晰度的多焦距图像融合算法.该算法定义了图像区域的相对清晰度判定指标,并根据多焦距图像像素邻域的相对清晰度来逐个选取聚焦清晰的像素点从而构成融合图像.实验表明,所提算法能取得优于其它算法的融合效果;特别是,它能较好保留源图像包含的细节信息,从而显著减弱融合结果中的"振铃"现象.     

基于区域锐度的多聚焦图像融合

为了获得同一场景内所有物体都清晰的图像,提出一种新的多聚焦图像融合算法。把待融合图像进行分块,构造融合块的清晰度评价函数（区域锐度）,根据其区域锐度值,判断融合块应取自哪幅源图像。采用投票选举的方法对融合图像进行一致性校验,对相邻但来自不同聚焦图像的融合块进行加权融合。实验结果证明,与基于小波分解的融合算法相比,该算法速度快、效果好。     

基于区域分割和非下采样Contourlet变换的多聚焦图像融合算法

针对基于神经网络分割算法计算复杂、运算量大等问题,提出一种根据单焦距图像聚焦区域和失焦区域局部相对清晰度的不同进行区域分割的多聚焦图像融合算法。该算法有效结合了非下采样Contourlet变换(NSCT)在图像分解中的多尺度、方向性、各向异性和平移不变性等特点,利用各方向高频分量的聚类来对低频分量进行分割、融合。实验表明该算法是一种有效的多聚焦图像融合方法。     

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。     

基于图像矩函数的图像清晰度评价方法

自动调焦作为显微图像测量中的关键技术,其核心为图像清晰度评价函数。现有图像清晰度评价函数在对焦目标与背景的对比度较低时,评价曲线极易失去其理想特性,或灵敏度下降,造成聚焦精度的降低;或出现多个局部极值,造成调焦失败。针对这一问题,在分析离焦图像成像原理的基础上,提出一种新的基于图像矩的图像清晰度评价函数,并给出定义。新的评价函数由不同阶的图像矩加权构成,通过调节权重系数可以灵活控制评价函数的灵敏度和抗噪性。通过微小透明球体的显微图像序列对新的评价函数进行实验验证,并和四种常用评价函数进行分析比较。实验结果证明,在低对比度条件下,相对于其它四种常用评价函数,基于图像矩的图像清晰度评价函数在有效保持了如单峰性、无偏性等理想曲线特性的同时,在灵敏度及抗噪性方面也具有明显优势。     

利用局部极值点特性的多聚焦图像快速融合

目的：多聚焦图像融合技术一个关键问题是如何准确地判断待融合图像的清晰度。本文提出了基于归一化结构极值点数目的清晰度判断准则。方法：本文基于图像的局部极值点特性,定义了归一化结构极值点数目这个指标作为清晰度判断准则,同时还给出了利用该准则和融合决策矩阵快速估计技术的多聚焦图像快速融合方法。结果：利用本文提出的清晰度判断准则和融合方法,实验表明上述问题得到了很好的解决。结论：本文提出了一个新的图像清晰度判断准则,该准则判断准确率高,且对脉冲噪声有好的鲁棒性。通过与传统融合方法对两组实验图像融合结果的主客观比较表明,该方法的融合速度和效果比现有多聚焦图像融合方法有明显提高。     

基于二次成像与清晰度差异的多聚焦图像融合

本文提出了一种基于清晰度差异的不同聚焦点图像的融合方法。该方法首先选择了一种基于梯度向量模方和的清晰度定义,然后根据几何光学系统的成像模型,以及点扩散函数的作用效果提出了模拟光学系统的二次成像模型。然后根据二次成像前后各图像清晰度的差异情况,对各幅图像中的目标进行判断,并选择其中的清晰部分生成融合图像。实验结果表明,该方法可以提取出多聚焦图像中的清晰目标,生成的融合图像效果优于Laplacian塔型方法和小波变换方法。     

基于分数阶混合蝙蝠算法的Otsu图像分割

针对传统蝙蝠算法在图像阈值分割时存在的分割精度低、效果差、算法收敛速度慢等缺点,提出基于分数阶混合蝙蝠算法的Otsu图像分割算法.将分数阶微积分、天牛须搜索与传统蝙蝠算法相结合,利用分数阶微积分的遗传特性均衡蝙蝠的全局搜索过程,提高寻优能力.利用天牛须搜索丰富蝙蝠局部搜索时种群的多样性,加快高精度下算法的收敛速度.将二维Otsu算法的离散度测度矩阵作为寻优的目标函数进行图像阈值分割.实验结果表明,改进后的算法提升了图像的分割精度与效果,且有着更快的收敛速度和良好的健壮性.     

All-in-focus imaging using average filter-based relative focus measure

Digital images are normally taken by focusing on an object, resulting in defocused background regions. A popular approach to produce an all-in-focus image without defocused regions is to capture several input images at varying focus settings, and then fuse them into an image using offline image processing software. This paper describes an all-in-focus imaging method that can operate on digital cameras. The proposed method consists of an automatic focus-bracketing algorithm that determines at which focuses to capture images and an image-fusion algorithm that computes a high-quality all-in-focus image. While most previous methods use the focus measure calculated independently for each input image, the proposed method calculates the relative focus measure between a pair of input images. We note that a well-focused region in an image shows better contrast, sharpness, and details than the corresponding region that is defocused in another image. Based on the observation that the average filtered version of a well-focused region in an image shows a higher correlation to the corresponding defocused region in another image than the original well-focused version, a new focus measure is proposed. Experimental results of various sample image sequences show the superiority of the proposed measure in terms of both objective and subjective evaluation and the proposed method allows the user to capture all-in-focus images directly on their digital camera without using offline image processing software.     

A dedicated hardware architecture for real-time auto-focusing using an FPGA

The auto-focus is a fundamental function of a camera system which is required to photograph a clear image of an object. To obtain the optimal focus of a specific region within an image, the sharpness of the region must be measured. Since the sharpness represents the difference between a pixel and its neighbors, multiple pixel references occur while evaluating each pixel. To compensate for the processing bottleneck caused by this repetitive memory reference, this paper presents a dedicated hardware architecture for real-time auto-focusing. The proposed system processes the incoming pixel simultaneously with its neighboring pixels based on its parallelized window processing architecture. In addition, the proposed system performs an adaptive thresholding-based sharpness function with multiple windows to achieve accuracy and robustness. The proposed system is compared to several conventional pixel-based auto-focusing systems under various environmental conditions.     

数字视频监控系统中实时运动检测系统的研究

在数字视频监控系统中,从视频流里捕获出序列图像并进行实时运动检测是一项重要的功能。采用两个进程分别实现视频图像的捕获和运动检测,命名管道作为两个进程之间数据通信的桥梁。提出了一种基于帧差交集快速视频分割法、阈值面积算法结合数学形态学运算提取运动目标区域的数字图像处理算法,然后再进行运动目标的跟踪定位,实现实时运动检测。     

Smart Depth of Field Optimization Applied to a Robotised View Camera

The great flexibility of a view camera allows the acquisition of high quality images that would not be possible any other way. Bringing a given object into focus is however a long and tedious task, although the underlying optical laws are known. A fundamental parameter is the aperture of the lens entrance pupil because it directly affects the depth of field. The smaller the aperture, the larger the depth of field. However a too small aperture destroys the sharpness of the image because of diffraction on the pupil edges. Hence, the desired optimal configuration of the camera is such that the object is in focus with the greatest possible lens aperture. In this paper, we show that when the object is a convex polyhedron, an elegant solution to this problem can be found. It takes the form of a constrained optimization problem, for which theoretical and numerical results are given. The optimization algorithm has been implemented on the prototype of a robotised view camera.     

基于BZX-1系统的自适应图像处理快速算法设计

提出了基于BZX-1系统的实时图像处理的快速自适应算法本算法由快速图像分割、快速弹孔识别和快速成绩计算3个模块组成。快速图像分割模块的功能是用削波投影算法，从靶位图中分割出胸环靶；在快速弹孔识别模块中，基于图像区域聚类，设计了自适应模糊弹孔识别算法；快速成绩计算模块是利用从参考圆心到弹孔点的灰度跃变计算环数。     

基于图像的大型物体尺寸的三维测量方法研究

针对大型物体尺寸不易测量的问题,研究简单快速的测量方法;提出了一种利用数码相机完成物体尺寸的三维测量的方法;利用数码相机从多角度获取图像,采用稳定性强的Harris角点检测算法对图像进行特征点检测;在相机标定过程中提出了一种利用图像中被测物体自适应寻找标定模板的方法,扩大了标定模板的使用范围;最后利用投影矩阵理论计算特征点的世界坐标进行测量;实验证明测量结果误差在2.0%以内,说明该方法能够克服传统测量方法的缺点,满足一般工业的测量要求并且适用范围广。     

应用于聚焦窗口自适应选择的人工鱼群算法改进

聚焦窗口选择是自动聚焦算法中的重要模块,直接影响评价函数曲线的尖锐性、精确性,影响聚焦系统的复杂度。针对传统的聚焦区域选择算法的局限性,提出了一种基于AFSA的聚焦区域自适应选择算法;针对标准的人工鱼群算法易陷入局部最优的缺陷,对人工鱼群算法中的参数设置及行为进行改进。实验表明,采用人工鱼群算法可快速实现效果较好的前景图像分割,通过选取前景边缘丰富的区域,保证了前景聚焦的精确性;算法可以跟踪偏离中心的主体景物,适用于主体与背景对比度小的情况,因此得到的聚焦区域是动态的,具有自适应性。     

运动模糊情况下障碍物检测预处理

针对在非结构化环境下,摄像机和拍摄目标相对运动造成的图像模糊情况,进行障碍物检测预处理。利用边缘锐度法判定获得的图像是否为运动模糊图像,如果是则利用傅里叶频谱和Radon变换得到点扩展函数后,利用 Lucy-Richardson法进行复原;为了降低后期障碍物检测的处理复杂度,利用提出的一种改进二维熵分割算法进行障碍物粗定位,面积阈值消除噪声影响,完成障碍物检测预处理。通过仿真实验验证了该方法的可行性和有效性,为智能汽车在非结构化环境下避障提供参考依据,为障碍物识别奠定基础。     

基于新型DSP-BF533的嵌入式运动图像目标跟踪系统的设计

该文提出了一种利用最新高频DSP的强大的运算功能,运用嵌入式的方法裁减软硬件,并将裁减后的硬件系统放置在球机的内部,设计并实现了在使用单颗DSP下完成普通环境下对单个运动图像目标的实时跟踪。     

Multi-scale weighted gradient-based fusion for multi-focus images

Anisotropic blur and mis-registration frequently happen in multi-focus images due to object or camera motion. These factors severely degrade the fusion quality of multi-focus images. In this paper, we present a novel multi-scale weighted gradient-based fusion method to solve this problem. This method is based on a multi-scale structure-based focus measure that reflects the sharpness of edge and corner structures at multiple scales. This focus measure is derived based on an image structure saliency and introduced to determine the gradient weights in the proposed gradient-based fusion method for multi-focus images with a novel multi-scale approach. In particular, we focus on a two-scale scheme, i.e., a large scale and a small scale, to effectively solve the fusion problems raised by anisotropic blur and mis-registration. The large-scale structure-based focus measure is used first to attenuate the impacts of anisotropic blur and mis-registration on the focused region detection, and then the gradient weights near the boundaries of the focused regions are carefully determined by applying the small-scale focus measure. Experimental results clearly demonstrate that the proposed method outperforms the conventional fusion methods in the presence of anisotropic blur and mis-registration.