基于小波边缘提取的灰度图象联合相关识别预处理

本文将小波变换方法用于灰度图象的联合变换相关识别中,采用不同的尺度因子对输入图象进行边缘提取预处理,使相关识别结果得到不同程度的改善.通过计算机模拟对比了一阶、二阶微商的边缘提取方法和小波变换边缘提取方法的预处理结果和对识别的影响,在同时衡量相关识别能力及其对噪音的敏感性前提下,小波变换边缘提取预处理明显优于各种微商边缘提取方法.调节小波变换尺度因子还能使识别能力与噪音敏感性这两方面得到更好地均衡,使小波变换边缘提取预处理能够适应不同的图象输入条件和相关输出要求.结果表明,在联合变换相关识别中采用小波变换对输入图象进行预处理是一种更理想的方法。     

基于图像熵的各向异性扩散相干斑噪声抑制

本文提出了一种基于图像熵的各向异性扩散滤波方法. 该方法使用图像熵作为边缘检测算子, 避免了由于均值和方差等统计量的估计带来的误差, 提高了边缘检测能力. 试验结果表明该方法能够获得比传统方法更好的相干斑噪声抑制效果. 关键词： 相干斑抑制 各向异性扩散 图像熵 合成孔径雷达     

基于平稳小波变换的有噪弱目标检测方法的研究

利用平稳小波变换的多尺度边缘检测算法分别对模糊图像、低对比度图像和加入噪声的图像进行了边缘检测,验证了多尺度二次B样条小波的检测效果,也比较了三种小波局部模极大值方法在抗噪性、计算量及检测效果等方面的性能,并且针对对比度低,受噪声污染严重的目标图像,提出一种能够根据不同背景计算出自适应阈值的新方法,使其在抗噪的同时又能较好地提取出微弱目标边缘。实验证明,利用多尺度二次B样条小波边缘检测算法能有效地排除噪声干扰,准确地提取出微弱边缘,可以实现3%对比度下的有噪图像的目标探测问题。     

一种照明无关的小波多尺度相乘边缘检测方法

提出了一种照明无关的小波多尺度相乘边缘检测方法,用于从非均匀的弱照明图像中提取边缘。根据照明反射图像形成模板与CCD相机成像原理,推导出图像的对应小波变换公式。然后,对图像局部区域中噪声、边缘与背景像素的小波系数进行比较分析,设计了一种照明无关的小波边缘检测公式。为增强边缘并抑制噪声,提出了一种改善的小波多尺度相乘边缘检测方法,并依照小波变换后边缘像素的特征,提取单像素的边缘。采用仿真和真实的非均匀的弱照明图像对该边缘检测算法进行验证,并与另外两种边缘检测方法进行定性的和定量的比较。实验结果证实了这种边缘检测方法能够从灰度不均匀的低衬比度图像中正确有效地提取边缘。     

基于小波变换的激光主动成像图像去噪方法

 针对激光主动成像图像的特点,提出了将小波变换和中值滤波相结合的图像去噪方法。在对小波分解后的水平、垂直和对角3个方向高频细节图像进行处理时,我们采用3种不同形状的模板进行均值滤波,为了保护图像的边缘和细节信息,采用边缘检测法来将高频中的边缘细节与噪声分开。实验结果表明:该方法在降低图像噪声的同时又较好地保留了图像的细节,去噪效果比较理想。     

Improved multi-scale wavelet in pantograph slide edge detection

The mainstream methods of pantograph slide edge detection are based on canny operator and multi-scale wavelet. The former has good single edge response but the edge is fractured, the latter performs good edge continuity but contains excessive edge points. This paper combines the advantages of both methods and proposes as an improved multi-scale wavelet edge detection method based on canny criteria. Firstly we filtered the pantograph image with edge-preserving symmetric near neighbor filter. Secondly calculated the Gaussian wavelet modulus and arguments at all levels of scale, then suppressed the non-maxima value of modulus along the corresponding arguments. At last, we integrated the modulus drawings at all levels of scale, and connected edge with applicable dual-threshold. Experiments results show that the improved algorithm has both satisfactory performances in single edge response and edge continuity, it markedly improves the efficiency of edge detection algorithm. Peak signal to noise ratio (PSNR) analysis finds that the improved algorithm exceeds canny operator and traditional multi-scale wavelet edge detection. Moreover, it has higher positioning accuracy, clearer details and better noise performance.     

小波模极大值法与数学形态学边缘检测细化结果

图像边缘检测的关键是尽可能多的检测到边缘并且抑制噪声的同时,尽可能的满足单线的边缘定位精度;为此选取了一种融合小波模极大值和数学形态学的边缘检测方法来获取图像边缘;首先在对图像进行小波分解,分别利用模极大值法和多尺度多结构数学形态学方法来处理小波分解的高频分量和低频分量,利用差影法对二者的结果进行融合;然后利用大律法得到二值化图像,并用形态学边缘细化算法细化图像边缘得到最后结果;实验结果显示,融合的方法可以得到比较完善的边缘,经过二值化和边缘细化后,获得的单线宽边缘更加清晰,定位精度更高。     

基于最小各向同性小波滤波的图像清晰度识别

提出了基于最小各向同性小波滤波的图像清晰度识别方法,对二维最小各向同性小波滤波提取图像特征进行了研究.直接将原始图像通过带通小波滤波器G0获得图像边缘信息,结合图像能量分析,建立了基于小波滤波的图像清晰度评价函数.利用构建的显微镜自动对焦实验平台,比较分析了基于小波滤波和拉普拉斯的评价方法.实验结果表明,采用基于各向同性小波滤波的自动对焦算法有更好的综合自动对焦性能.     

各向异性扩散滤波远探测声波测井图像降噪方法*

声波远探测测井技术近年来在复杂油气藏的构造识别和储层评价中发挥着重要作用。该技术利用来自井外的反射波对井旁地质构造进行准确成像,但由于反射波具有幅度低、受幅度强的井孔直达波干扰等特点,实际数据提取到的反射波信噪比往往比较低,需要对反射波进行降噪处理。非线性各向异性扩散滤波能够在滤除图像噪声的同时保留图像边缘及细节等信息,在地震数据处理和医学图像去噪中都有广泛应用。该文从各向异性扩散滤波的基本原理入手,将提取到的井外反射波信号当作图像,采用不同扩散张量进行处理,通过含噪声的模拟数据处理验证了该方法的处理效果并建立起适合于远探测测井的数据处理流程,实际远探测数据处理结果进一步表明其具有较好的应用前景。     

一种基于小波分析的唐卡图像划痕检测

唐卡图像在其保存和搬运过程中较易出现细微的划痕,针对所出现的垂直(水平)划痕进行了自动检测算法研究。垂直(水平)划痕实质上属于特殊的边缘,首先利用该特性采用小波模极大值描述图像中目标的多尺度边界;然后再通过投影变换放大划痕中心的亮度极值特性,并基于多尺度突变点检测找到划痕的位置;最终采用标注联通分量、设置高度和宽度约束得到划痕的掩膜。实验结果表明,利用该方法取得了较好的检测效果。     

The adaptive sinogram restoration algorithm based on anisotropic diffusion by energy minimization for low-dose X-ray CT

Apparent streak artifacts will present in reconstructed images due to excessive quantum noise in low-dose X-ray imaging process. Estimating a noise-free sinogram to satisfy the filtered back-projection (FBP) reconstruction is an effective way to solve this problem. In this paper, we propose a novel sinogram noise reduction method by energy minimization. An adaptive smoothness parameter based on a modified anisotropic diffusion coefficient is applied for an optimal estimation. The smoothness parameter can make the method effectively adjust the degree of smoothness according to the noise level and the region feature in the sinogram. Visual effect together with quantitative analysis of the experimental result shows the developed approach has the excellent performance in protection of the edge and removal of streak artifacts in the reconstructed image.     

一种基于扩展数学形态学的边缘检测方法

传统的边缘检测方法对噪声敏感,抑制噪声能力差;基于经典形态学的边缘检测方法虽然具有较好的去噪能力,但不能反映全部的边缘特性。为此给出了一种扩展的数学形态学的定义,并提出了一种基于扩展数学形态学的边缘检测方法。仿真结果表明,该方法不仅能有效地去除噪声,而且通过检测所得到的边缘图像具有良好的边缘特性。     

Target recognition in clutter scene based on wavelet transform

Edge extraction based on wavelet for optical correlation detection is presented. Optical experiments with joint transform correlator (JTC) show that there is a bright application prospect in the field of optical correlation detection by extracting the edge features of input image with the method of wavelet transform. In the course of processing, the multi-scale character of wavelet is used sufficiently. The energy of correlation peaks and the detection ratio of various targets are greatly enhanced by the approach. To demonstrate the feasibility of edges extraction based on WT, small targets and targets in clutter scene are successfully detected.     

Real-time application of critical dimension measurement of TFT-LCD pattern using a newly proposed 2D image-processing algorithm

A critical dimension measurement system for TFT-LCD patterns has been implemented in this study. To improve the measurement accuracy, an imaging auto-focus algorithm, fast pattern-matching algorithm, and precise edge detection algorithm with subpixel accuracy have been developed and implemented in the system.The optimum focusing position can be calculated using the image focus estimator. The two-step auto-focusing technique has been newly proposed for various LCD patterns, and various focus estimators have been compared to select a stable and accurate one.Fast pattern matching and subpixel edge detection have been developed for measurement. The new approach, called NEMC, is based on edge detection for the selection of influential points; in this approach, points having a strong edge magnitude are only used in the matching procedure. To accelerate pattern matching, point correlation and an image pyramid structure are combined.Edge detection is the most important technique in a vision inspection system. A two-stage edge detection algorithm has been introduced. In the first stage, a first order derivative operator such as the Sobel operator is used to place the edge points and to find the edge directions using a least-square estimation method with pixel accuracy. In the second stage, an eight-connected neighborhood of the estimated edge points is convolved with the LoG (Laplacian of Gaussian) operator, and the LoG-filtered image can be modeled as a continuous function using the facet model. The measurement results of the various patterns are finally presented.The developed system has been successfully used in the TFT-LCD manufacturing industry, and repeatability of less than 30 nm (3σ) can be obtained with a very fast inspection time.     

基于小波变换的图像融合新算法

针对现有图像融合技术中的尺度系数卷积法存在的边缘细节损失和对比度下降问题,提出了将Canny边缘检测算子等价于提取图像的小波变换模极大值点这一思想引入图像的低频分量融合规则以及用于高频子带融合的局部对比度结合方向方差方法.最后通过仿真试验以及熵值数据证明该算法有效改善了边缘细节的准确度,提高了分辨率,并使局部对比度信息得以保留,从而更加符合人类的视觉特性.     

基于小波变换和数学形态学的孔径测量研究

针对视觉测量中硬盘圆孔孔径容易受到圆孔周围局部强反射、外部噪声的影响和测量精度不高的问题,提出一种基于小波变换、数学形态学和机器视觉相结合的圆形零件孔径测量方法。对摄像头采集圆孔图片,通过小波变换分解出高低频图像,利用小波变换对高频部分进行边缘提取,数学形态学对低频部分进行边缘提取,然后进行小波边缘融合,获取有效的圆孔边缘。利用最小二乘法对边缘进行孔径尺寸计算,经实验验证,该算法与其他算法相比,在有效地保留圆孔周围边缘信息的情况下,对周围噪声进行了有效的抑制,检测精度为0.01 mm以内,实验结果表明此方法简单易行,且具有较高的精度。     

基于粗糙集与小波反锐化掩模的图像增强

为了有效去除图像中的干扰噪音,改善模糊不清晰的有用边缘细节信息,提高图像的层次感和清晰度,提出将粗糙集和小波反锐化掩模相结合的方法来实现图像增强.运用粗糙集中的近似及等价属性关系将知识化后的图像划分成不同的区域,再根据估计好的阈值进行数据约简,实现有效去除噪音.运用小波反锐化掩模法对图像的轮廓及细节信息进行增强处理,完成图像的最终增强.实验结果表明,不论在主观的视觉效果上,还是客观的噪音均方误差,该方法的处理结果都是较为理想的.     

小波变换在CCD图像边缘检测中的应用

对于CCD图像的非稳定高斯噪声,传统的Fourier变换法无法消除噪声,而小波变换在时域和频域同时具有优良的局部化特征,而且对高频采用逐渐精细的时域或空城步长可以聚焦到分析对象的任意细节,从而克服了传统Fourier变换固定步长等缺点,因此,小波变换法可消除这种噪声.本文在分析CCD图像噪声的基础上,利用小波变换和直方图分割方法对目标图像进行了降噪和边缘检测.试验证明此法行之有效.     

基于静态小波分解和能量函数优化的图像拼接

提出了一种基于静态小波分解和能量函数优化的全景图拼接算法.该算法首先使用静态小波分解初始图像,并利用序贯检验法对高频图像快速搜索计算粗略匹配序列,在此基础上对初始图像细致搜索计算精确匹配点；对于传统图像融合处理中采用的线性加权函数通常引起的重叠区域模糊问题,采用包含图像梯度的能量函数计算高频图像的全局最优融合因子进行融合,而低频图像则采用线性因子融合.实验表明,该算法在降低匹配运算时间的同时又能获得满意的视觉效果.     

基于改进的空域相关的多聚焦图像融合

提出了一种简单有效的像素级多聚焦图像融合方法。针对正交小波变换缺乏平移不变性而产生视觉失真的缺陷,采用Atrous算法将原图像分解在不同频率域上。Atrous算法先将滤波器h0(n),h1(n)各点间插入适当的零值后再与低频信号做卷积,故又称为"多孔算法"。将具有抑制噪声性能的空阈相关法作为高频子图像的融合规则,选取相关性强边缘特征显著的点作为最终融合子图像的像素点。实验表明,由此融合的图像能完好的保留边缘纹理信息。融合后的图像在客观评价和主管视觉效果上均有提高。