基于同态滤波的电子散斑干涉图像处理

由于散斑具有可测的强度和确定的相位,正逐渐被人们重视和进一步研究,现广泛应用于无损检测领域。电子散斑干涉条纹图上总是附带大量的随机散斑颗粒噪声。一般来说,散斑图像记录的变形信息主要集中在低频部分,而噪声主要在高频部分。为了得到变形物体的连续相位分布,需要用适当的方法对散斑干涉条纹图像进行滤波降噪处理。同态滤波是一种将亮度范围压缩和对比度增强的频域处理方法。采用基于同态滤波原理设计出的新的低通滤波器,将其运用于3步相移电子散斑干涉条纹图像处理,并从理论上进行了详细的论述。结果表明,运用该滤波器处理散斑条纹图像,过滤了大量的散斑颗粒噪声,增强了散斑干涉条纹的对比度。     

一种新的干涉相位图局部自适应滤波方法

为了有效提高对InSAR干涉相位噪声的抑制性能并充分保持干涉相位图细节信息,该文提出一种基于局部地形相位补偿和各向异性高斯滤波函数(AGF)的自适应复相位滤波方法。该方法首先利用局部频率估计方法补偿地形相位,以便于消除局部地形相位对滤波窗口内干涉相位的不利影响。然后,构造了尺度和方向自适应的AGF,并对同分布样本进行局部加权的方向滤波。这里,AGF尺度随相干系数等级自适应变化:在低相干区域,采用的大尺度AGF能够充分地抑制相位噪声;在高相干区域,采用的小尺度AGF能更好地保持相位细节信息。AGF方向根据最大加权相干积累准则确定,以选取同分布的滤波样本估计中心像素相位值。实验结果表明,与多种滤波方法相比,该文方法在减少干涉相位图残点和保持条纹边缘等方面均具有更好的性能。     

像面散斑平均尺寸对散斑干涉条纹图的影响

散斑计量中,为了在动态测量中得到清晰的原始散斑条纹图,使更有效的提取相位信息,提出一种改变像面散斑平均尺寸大小对散斑成像影响的方法,进而改善原始散斑条纹图的清晰度。通过分析光路参数F数和放大倍数与像面散斑尺寸大小的定量关系,通过实验进行了验证。实验结果表明,形变量一定,系统放大倍数为1,成像透镜F值为4.5,或者,当F数为2,系统放大倍数为3.5,像面散斑平均尺寸大小为5.841μm,最接近像面尺寸5.86μm成像效果最好,经正余弦算法滤波后,得到的散斑条纹图最清晰,证实了像面上散斑尺寸大小对散斑条纹图的影响。     

基于MATLAB的数字散斑条纹图滤波比较

数字散斑照相术提取的数字散斑条纹图存在信噪比低、强度弱的特点,因此需要对图像进行滤波除噪。为了比较不同的滤波方法对散斑条纹图的除噪效果,笔者在MATLAB中采用中值滤波、巴特沃斯低通滤波、指数低通滤波以及同态滤波对提取的条纹图进行处理并就处理结果做出了分析比较。实验结果表明,以上方法都能从噪声中提取信号,其中中值滤波除噪效果最好。     

基于小波变换的ESPI图像去噪及边缘提取

电子散斑干涉条纹的强噪声特性使其信噪比过低,常用的图像滤波方法对于散斑干涉条纹都存在一定的不足。针对散斑条纹的特点,建立了自适应滤波与小波变换相结合的组合迭代滤波方法。在对散斑条纹预处理基础上,通过选择不同的小波函数以及更改分解层次和函数中的阈值达到不同的滤波效果。经反复试验,对于不同的小波基,采用4层分解,阈值为0.15～0.3时与自适应滤波的迭代效果最好。在滤波的基础上对图像进行了二值化,并采用Sobel算子对其进行边缘提取,最终得到电子散斑干涉条纹的边缘分布图。结果表明,该方法可以有效消除条纹图中的散斑噪声,并且条纹的边缘得以较好的保留。     

去除椒盐噪声的非对称有向窗加权均值滤波

针对传统滤波对称窗口在图像边缘处会引入干扰像素引起图像模糊的问题,提出一种非对称有向窗加权均值滤波算法.首先,基于区域极值进行噪声检测;其次,在对称有向窗的基础上提出非对称有向窗的概念,对于噪声点,通过标准差最小的原则自适应选择非对称有向滤波窗口;然后,在选择的非对称有向滤波窗口内对噪声点进行自适应基于距离倒数的加权均...     

结合非连续性测度的方向偏微分方程在电子散斑干涉中的应用

近年来,电子散斑干涉测量技术(ESPI)在光学粗糙表面的变形测量和无损检测方面应用广泛。应用该技术时,条纹图中大量的散斑噪声给提取条纹信息带来了极大的困难。偏微分方程图像滤波方法是一种方案灵活、处理效果良好的图像去噪方法,尤其是方向偏微分方程,因其只沿着条纹方向进行滤波,更适合电子散斑干涉条纹图。由于条纹图的疏密程度不同,因此在考虑滤波方向的同时,还应考虑不同位置像素点的滤波程度。该文在方向偏微分方程基础上引入非连续性测度,提出结合非连续性测度(DCM)的方向偏微分方程。利用该方程对模拟的条纹图以及实际获得的条纹图进行滤波,实验结果表明该文方法能够充分滤除稀疏条纹处的噪声,同时有效保持密集条纹处的重要特征。     

能量分布识别相位畸变的自适应双域滤波方法

在基于能量分布识别相位畸变方法中,条纹图像的质量是影响畸变识别精度的主要因素之一。为了准确辨别条纹相位畸变,需对条纹图像进行滤波处理,滤除图像中的噪声以及背景信息等。提出了一种基于能量分布的自适应图像滤波方法,通过傅里叶变换将图像转换为能量分布图,根据畸变与未畸变图像能量弥散度差值大小对小波滤波的阈值进行调整,旨在寻求能量弥散度差值最大的阈值作为针对当前图像的滤波方法可接受阈值。通过对单空域、单频域以及双域滤波方法进行实验验证,结果表明单一使用空域或频域滤波处理,畸变图像与未畸变图像能量分布差别在1.4 %~3.2 %,使用非自适应双域滤波方法进行滤波处理,能量分布差别在7 %以上,而采用自适应双域滤波方法进行滤波处理,能量分布差别超过8 %。自适应双域滤波方法显著提高了能量的识别精度。     

Toward edge sharpening: a SAR speckle filtering algorithm

This paper makes two contributions. It first introduces an algorithm for synthetic aperture radar (SAR) speckle reduction and edge sharpening. Existing speckle filtering algorithms can effectively reduce the speckle effect but unfortunately also, to some degree, smear edges and blur images. Even for unfiltered images, there is still a need for edge sharpening; since SAR sensors have limited bandwidths, leading to slow responses to sudden changes (smearing sharp edges). The proposed algorithm functions as an adaptive-mean filter. Edge crossing points are detected by using the second-order derivative of the Gaussian function as a wavelet transform function. A proper dilation scale factor enables the wavelet transform function to detect only edge crossings and ignore the local oscillations. Then in a moving window the mean filter is applied if there is no edge crossing point. Otherwise, averaging is only applied to the part of the window separated by edge crossing points. Consequently, the algorithm smooths uniform areas while it sharpens and enhances edges. Edges of the filtered images are generally sharper than the original. Similarities between the proposed filter and other popular speckle filters, such as the Lee, Kuan, and Frost filters, designated for SAR multiplicative noise, are analyzed. Another contribution of the paper is the evaluation of popular filters from the viewpoint of texture preservation. The evaluation is carried out using the first and second-order histograms. Possible distortions caused by filters are explained     

激光主动探测图像散斑自适应滤波抑制方法研究

激光主动照明成像具有作用距离远、系统分辨率高、可在低照度背景等复杂环境下获取目标图像等优点,但探测图像会受散斑噪声干扰.把高斯滤波、均值滤波和自适应滤波方法分别应用到仿真实验中进行散斑噪声抑制,实验表明:与高斯滤波和均值滤波相比,自适应滤波能有效抑制图像噪声,保留图像的边缘和细节信息.利用自适应滤波方法对获取的单帧和多帧累加平均的激光主动探测图像进行散斑抑制实验,使用散斑对比度进行定量分析,结果表明多帧短曝光图像累加平均可有效抑制图像的散斑噪声,自适应滤波可进一步降低图像的散斑噪声.     

激光无损检测中基于调制度的图像自适应滤波研究

激光散斑图像在成像时不可避免地受噪声乘性调制,因此去除噪声是激光散斑图像处理的一项重要任务.本文在分析和对比了不同滤波算法的特点之后,基于调制度能够度量像素可靠程度,结合均值滤波,提出了基于调制度的条纹图像自适应均值滤波算法,并将其应用在激光无损检测图像去噪中,实验结果表明该算法优于普通的均值滤波,在抑制散斑噪声的同时有效保护了边缘等细节信息.     

基于Goldstein滤波的数字散斑条纹图降噪方法

有效去除数字散斑条纹图中的噪声是散斑干涉测量技术中的关键问题。提出了一种基于Goldstein滤波的数字散斑条纹图平滑方法。该方法需要将散斑条纹图中的干涉相位转换为矢量空间中的单位矢量,并进行快速傅里叶变换(FFT),得到其频谱,然后对频谱进行加权处理,从而抑制噪声的频率成分,再将加权处理后的频谱变换到空间域,计算干涉相位,得到原始散斑条纹图的滤波结果。将该滤波方法运用于四步相移数字散斑干涉条纹图像处理。实验结果表明,该方法在滤除散斑噪声的同时能够有效地保护散斑条纹图的轮廓和细节信息,增强了散斑干涉条纹的对比度。     

Speckle filtering of SAR images based on adaptive windowing

Speckle noise usually occurs in synthetic aperture radar (SAR) images owing to coherent processing of SAR data. The most well-known image domain speckle filters are the adaptive filters using local statistics such as the mean and standard deviation. The local statistics filters adapt the filter coefficients based on data within a fixed running window. In these schemes, depending on the window size, there exists trade-off between the extent of speckle noise suppression and the capability of preserving fine details. The authors propose a new adaptive windowing algorithm for speckle noise suppression which solves the problem of window size associated with the local statistics adaptive filters. In the algorithm, the window size is automatically adjusted depending on regional characteristics to suppress speckle noise as much as possible while preserving fine details. Speckle noise suppression gets stronger in homogeneous regions as the window size increases succeedingly. In fine detail regions, by reducing the window size successively, edges and textures are preserved. The fixed-window filtering schemes and the proposed one are applied to both a simulated SAR image and an ERS-1 SAR image to demonstrate the excellent performance of the proposed adaptive windowing algorithm for speckle noise     

提高傅里叶变换轮廓术测量精度的新方法

在傅里叶变换轮廓术 (FTP)测量方法中 ,成像装置获取受到物体高度调制后的变形结构光场 ,通过傅里叶变换 ,滤波和逆傅里叶变换恢复出物体高度分布。频谱滤波是FTP中很重要的一个过程 ,也是影响FTP测量精度的一个重要因素。通常滤波是手工选择合适的滤波函数和滤波窗口大小 ,窗口太小 ,表示物体细节的高频信息将丢失 ;窗口太大 ,无法消除噪声。本文提出一种新的滤波方法 ,无需手工选择滤波函数 ,可以很好地保留物体高频信息并消除随机噪声 ,从而提高FTP的测量精度。计算机模拟和实验证实了所提方法的有效性     

一种新的SAR图像相干斑抑制算法

针对经典局域统计自适应滤波器的窗口大小问题,提出了一种新的用于抑制合成孔径雷达图像相干斑噪声的滤波算法.基于SAR图像的统计特性,窗口大小根据区域特征自动调整,尽可能将窗口调整为包含同一均匀区域,从而在保持细节的同时尽最大可能地抑制斑点噪声.实验结果表明,本算法不仅有效抑制了SAR图像的相干斑,保持图像的边缘细节.而且有着良好的图像视觉效果.     

基于窗口的自适应中值滤波算法

滤波窗口是影响椒盐噪声滤除效果的重要因素。针对自适应中值滤波算法(RAMF)的不足,提出了一种基于窗口的自适应中值滤波算法。该算法分为噪声的检测和滤除两部分。在噪声检测部分,主要通过混合窗口检测出准噪声点,将其用窗口中值代替,而其余信号点保持不变。重复此方法,直至所有的准噪声点处理完毕。然后,在噪声滤除部分,主要根据噪声密度选择合适的最大滤波窗口半径,进而实现噪声滤除。最后,为验证算法的有效性开展了仿真研究,仿真结果表明本算法对椒盐噪声的滤除具有很好的效果,增强了图像的清晰度。     

一种新的高分辨率SAR图像相干斑噪声抑制算法

提出了一种新的基于小波变换对高分辨率合成孔径雷达图像相干斑噪声抑制的算法。首先从SAR图像相干斑噪声产生的机理出发，论述了通过传统的滤波方法在抑制高分辨率SAR图像的相干斑噪声时，损失了大量的边缘信息和纹理细节而采用小波变换降噪的优越性和必要性；其次论述了在小波域中如何利用高频局部的统计特性和分解尺度大小来选取滤波窗口尺寸进行滤波；最后通过实验结果说明了该方法比采用传统的固定窗来实现对高分辨率SAR图像的降噪、保留边缘信息和纹理细节有着更好的性能。     

Two-dimensional adaptive block Kalman filtering of SAR imagery

A method for removing speckle from synthetic aperture radar (SAR) imagery by using 2-D adaptive block Kalman filtering is introduced. The image process is represented by an autoregressive model with a nonsymmetric half-plane (NSHP) region of support. New 2-D Kalman filtering equations are derived which taken into account not only the effect of speckles as multiplicative noise but also the effects of the additive receiver thermal noise and the blur. This method assumes local stationarity within a processing window, whereas the image can be assumed to be globally nonstationary. A recursive identification process using the stochastic Newton approach is also proposed which can be used on-line to estimate the filter parameters based upon the information within each new block of the image. Simulation results on several images are provided to indicate the effectiveness of the proposed method when used to remove the effects of speckle noise as well as those of the additive noise     

Biased motion-adaptive temporal filtering for speckle reduction in echocardiography

Describes a new fully motion-adaptive spatio-temporal filtering technique to reduce the speckle in ultrasound images. The advantages of this approach are demonstrated in echocardiographic boundary detection and in comparison with other techniques. The first stage of many automated echocardiographic image interpretation schemes is filtering to reduce the amount of speckle noise. The authors show how the two-dimensional least mean squares (TDLMS) filter can be configured as a motion-compensated filter for a time sequence of ultrasound images that eliminates the blurring associated with direct averaging. For an image corrupted by multiplicative speckle noise, the mode of the intensity distribution approximates the maximum likelihood estimator. In consequence, the temporal filter's output is biased towards the mode from the mean, using information contained within the speckle itself. A new adaptive algorithm for controlling the filter's convergence is also included. To evaluate performance, application to simulated, phantom, and an in vivo test sequence of the carotid artery are considered in comparison with other techniques. The effect of filtering on edges is of great importance, as these are used by subsequent image interpretation schemes. Quantitative measurements demonstrate the effectiveness of the Biased TDLMS filter, for both noise reduction and edge preservation. Echocardiographic images have a high noise content and suffer from poor contrast. Despite this challenging environment, the Biased TDLMS filter is shown to produce images that are better inputs for subsequent feature extraction. The benefits for echocardiographic images are highlighted by considering the problems of mitral valve analysis and extraction of the left atrium boundary.     

基于混合迭代滤波的SAR图像相干斑抑制

该文将变窗Lee滤波与自蛇扩散结合,提出了一种抑制SAR图像相干斑噪声的混合迭代滤波新算法,并给出了SAR图像噪声方差的一种有效估计方法。该算法首先估计SAR图像局部统计参量,进而通过改进的变窗Lee滤波对SAR图像进行平滑去噪,接着利用自蛇扩散去除Lee滤波难以平滑的孤立点噪声与边缘噪声。在Lee滤波与自蛇扩散的混合迭代中,Lee滤波的窗尺度随迭代次数的增加而增大,从而在保护SAR图像边缘细节的同时,同质区得到更好平滑。实验结果表明,与已有多种抑斑算法相比,该文算法在SAR图像抑斑与边缘保护方面均获得了更好的性能。