Image noise reduction using Wiener filtering with pseudo-inverse

We present a preliminary design and experimental results of a Gaussian noise reduction method for ultrasound images. Our method utilizes a Wiener filtering algorithm with pseudo-inverse technique. The method is capable of solving the Gaussian noise problem in ultrasound image by setup a constant dB of noise function. The key idea of the Wiener filtering algorithm is to process the given ultrasound signal by making the filtering less sensitive to slight changes in input conditions. In this paper, we investigate the possibility of employing this approach for pre-processing ultrasound image application. The application of the proposed method for reducing Gaussian noise is demonstrated by four examples. Meanwhile, we also made the comparisons with median filter, mean filter and adaptive filter; the results reveal that the proposed method has the best noise filtering capability than other three methods. The results also show that the proposed method produces recovery images with quiet high peak-signal-to-noise ratio.     

基于脉冲耦合神经网络模型的小波自适应斑点噪声滤除算法

分析了维纳滤波原理和脉冲耦合神经网络(PCNN)模型的特点,根据斑点噪声统计模型的特征,结合小波变换方法,提出了一种基于PCNN模型的小波自适应斑点噪声滤除算法(W-PCNN-WD)来改善超声图像质量.首先,对超声图像进行对数变换,使斑点噪声转换为加性噪声;对医学图像进行维纳滤波处理,计算其加性噪声的标准方差,并以此作为小波阈值.然后,利用小波变换对图像进行预处理,利用PCNN在小波域中对小波系数进行相应的修正.最后,进行小波逆变换和指数变换,获得滤除噪声的图像.结果表明:本文提出的滤波方法优于其他滤波方法,当噪声方差为0.01时,本文滤波算法获得的峰值信噪比(PSNR)比经Wiener滤波方法获得的高出9 dB.该滤波方法能在有效去除超声斑点噪声的基础上保留图像的边缘细节信息,极大地改善了图像的视觉质量.     

利用空间光调制器的衍射特性提高VLC输出相关峰值

VLC中,由于SLM像素的周期性结构,在匹配滤波面上产生目标图像的多级衍射谱。通过在不同级衍射谱处加载匹配滤波器进行匹配滤波,理论分析表明各级谱输出的相关点在输出面上位置相同,相干叠加的结果使输出相关峰值提高。实验验证表明该方法能有效提高VLC的输出相关峰值。     

一种基于NSCT变换的超声图像降噪新算法

为有效抑制超声仪器成像中固有的斑点噪声,提出了一种基于非降采样Contourlet变换(nonsubsampled Contourlettransform,NSCT)域中边缘信号系数区提取和最小均方误差(minimum mean square error,MMSE)估计的超声图像的降噪算法。根据NSCT变换的细节信息刻画能力和平移不变性,对其各高频子带中系数进行分类,提取出边缘信号和平缓信号系数区;对超声图像的乘性斑点噪声进行推导研究,在边缘信号系数区和平缓信号系数区,根据各自噪声项的性质分别得出满足贝叶斯最小均方误差估计的降噪滤波方程;最后,对降噪后的系数进行NSCT反变换重建得到降噪图像。仿真图像和临床超声图像的实验结果证实,该算法与传统方法相比,不但能更有效地对斑点噪声进行抑制,也更好地保留了图像的细节信息。     

基于听觉原理的谐波频率自动提取方法

基于听觉系统的运行机制,提出一种可自动识别和提取信号中谐波频率的计算方法。首先,对信号进行基底膜带通滤波;其次,模拟听皮层时频感受野现象对不同时频域内信号进行二维傅里叶变换并提取幅值和频率信息,基于幅值信息的侧抑制结果,提出了一种谐波成分频率点的判据方法;最后,对提取得到的谐波频率点进行重组,从而可揭示信号中的主要频率成分以及各成分的时变情况。数值仿真和试验数据验证结果表明,所提方法可准确提取信号中的各谐波频率,并具有一定的抗噪声干扰能力,对于微弱谐波成分的频率提取亦有较好效果。     

Early fault detection method for rolling bearing based on multiscale morphological filtering of information-entropy threshold

The scale of structure element is especially important to obtain good filtering results in multiscale morphological filtering (MMF) method. In general, the optimal scale of structure element is set to be a fixed value in traditional morphological filter, therefore it is difficult to extract the fault feature from rolling bearing vibration signal effectively. A novel multiscale morphological filtering algorithm is proposed based on information-entropy threshold (IET-MMF) for early fault detection of rolling bearing. Compared with traditional MMF method, several optimal scales of structure elements are achieved according to the energy distribution characteristic of different vibration signals. The information entropy theory is applied to quantify the analyzed signals, and the optimal threshold of information entropy is obtained by iterative algorithm to ensure integrity of useful information. The simulation and rolling bearing experimental analysis results show that the IET-MMF method can extract fault features of vibration signals effectively.

     

基于相位和GGVF的水平集乳腺超声图像分割

提出一种基于相位和广义梯度矢量流(generalized gradient vector flow,GGVF)的水平集分割方法,并用于乳腺超声图像的肿瘤分割。首先,在频域空间,用Cauchy核替代Log-Gabor作为正交滤波器对图像进行滤波,提取来自于单演信号的多尺度图像特征,引入相位一致的思想将多尺度特征结合起来进行边界检测;然后,在此基础上,利用相位一致梯度图定义了一个基于相位的速度停止项函数,同时改进了GGVF;最后,将得到的速度停止项和梯度矢量流融入到水平集演化方程中来控制曲线的演化,获得乳腺肿瘤的边界。实验结果表明,使用该分割方法可获得比现有方法更好的乳腺肿瘤分割结果。     

匹配压缩脊线提取在齿轮箱故障诊断中的应用

匹配解调技术是一种新的时频分析方法,在处理非平稳信号的时频特性方面,具有较高的时频分辨率。基于此技术,提出了匹配压缩脊线提取技术用于旋转机械非平稳工况下的故障检测。结合匹配解调技术与同步压缩技术,从多分量信号频谱中提取基频;同时提出旋转算子滤波技术与包络线滤波技术,移除强能量分量信号,得到弱能量分量信号的脊线。运用此方法,当基频能量非最大且故障信号能量较小时,能同时提取基频与故障特征信号。仿真分析和实例分析表明,该方法优于一般的脊线提取方法。将该方法应用于行星齿轮箱上,可有效提取故障特征分量,并与基频进行较为直观的比较。此方法可用于变转速工况下行星齿轮箱的故障诊断。     

自适应窗口形状的中值滤波

中值滤波去除噪声的同时,难免出现含有线形图像的边缘轮廓信息跟随丢失的情况。为了达到滤除图像噪声最大化,有用信息损失最小化的目的,根据图像边缘特征选择或设计了适当大小和形状的窗口来进行中值滤波等操作。一方面,创新性地提出将Hough变换应用到车轮裂缝CT图像的滤波窗口形状的选择上,针对单一方向轮廓的图像,利用Hough变换检测出裂缝的方向,从而采用与裂缝形状相应的窗口对裂缝进行有针对性滤波,将该方法与传统方法进行了对比实验,并将60层滤波后切片图像堆栈得到了三维图像;另一方面,针对含有多个方向轮廓的图像,为了进一步改善滤波效果,提出根据像素梯度设计斜向滤波器,对多方向的线对进行滤波,数据显示经改进方法滤波后图像的峰值信噪比(PSNR)较传统的中值滤波提高了4~6,结构相似性(SSIM)提高了1%~2%左右,最后用683层荞麦切片CT图堆栈得到三维图像,对比滤波前后图像,可知该方法滤波效果良好。     

基于改进SIFT算法的超声图像配准研究

针对SIFT算法对医学超声图像提取关键点数目较少的缺点,本文提出了采用HARRIS角点改进SIFT算法的方法,有效提取更具有结构意义的关键点数目,提高超声图像配准精度。此外,为进一步提高配准精度,增加一步互信息算法对图像进行精配。实验结果表明,本文算法是一种快速、高精度、全自动的医学图像配准算法,并可有效地提高配准精度。     

基于 S-G 滤波与包络提取算法的半导体激光器自混合干涉微位移测量

针对在半导体激光器自混合干涉( SMI )微位移测量中,测量噪声对 SMI 信号条纹计数极易引入误差,提出一种基于 Savitzky Golay ( S-G )滤波与包络提取的 SMI 信号处理算法,对自混合信号处理并识别条纹后,干涉条纹能够得到准确地计数。该算法采用 S-G 卷积均值滤波处理、三次样条函数插值包络分析和信号归一化处理,可以有效消除噪声高频信号和提取信号包络。采用电压调制模式下驱动压电陶瓷( PZT )产生微振动作为模拟振动源,实验中设置 3 组不同电压调制幅值下的振动源,采集并分析 SMI 信号及相应振幅信息。在 3 组实验当中,经过算法处理过的 SMI 信号,其干涉条纹数与电压调制 PZT 振动幅值由处理前偏离线性关系到处理后保持线性关系。实验结果表明,经 S-G 滤波与包络提取算法处理过的 SMI 信号能真实反映微米量级的微位移。     

Reconstruction of off-axis electron holograms using a neural net

In off-axis electron holography, the information about the specimen contained in amplitude and phase of the image wave is recorded by superimposing a reference wave to the image wave and recording the resulting interference pattern, called a hologram. The image wave is usually retrieved from this hologram by a filtering process in Fourier space. An alternative approach is to calculate the image wave pixel by pixel from the intensities in the hologram. In this paper, we describe a neural network that performs this step. As neural networks can represent nonlinear relations between the input and output data, this reconstruction process can use the nonlinear part of the information contained in the hologram and is thus less sensitive to noise.     

超宽带步进变频雷达人体呼吸检测方法研究

针对生命体检测技术中呼吸频率检测问题进行了研究,提出了利用超宽带步进变频雷达对生命体在一维距离域上成像的同时检测其呼吸频率的方法。该方法首先利用步进频率的回波数据得到目标的一维距离像,然后在一维距离像中目标信号两边作滤波,滤波后可将微弱的呼吸频率从噪声中提取出来,实现检测生命体呼吸频率的目的。该方法可将载有呼吸频率信号的目标一维像从距离域中截取出来,使得人体微动以及其他距离域上的干扰频率被抑制掉,提高了回波信号的信噪比。     

Extraction of yarn positional information from three-dimensional CT image of textile fabric using a yarn model for its structure analysis

This paper proposes a novel method for analyzing a textile fabric structure to extract positional information regarding each yarn using three-dimensional X-ray computed tomography (3D CT) image. Positional relationship among the yarns can be reconstructed using the extracted yarn positional information. In this paper, a sequence of points on the center line of each yarn of the sample is defined as the yarn positional information, since the sequence can be regarded as the representative position of the yarn. The sequence is extracted by tracing the yarn. The yarn is traced by estimating the yarn center and direction and correlating the yarn part of the 3D CT image with a 3D yarn model, which is moved along the estimated yarn direction. The trajectory of the center of the yarn model corresponds to the positional information of the yarn. The application of the proposed method is shown by experimentally applying the proposed method to a 3D CT image of a double-layered woven fabric. Furthermore, the experimental results for a plain knitted fabric show that this method can be applied to even knitted fabrics.     

离焦模糊图像的维纳滤波复原研究

微操作中，显微视觉系统获取的图像通常是离焦模糊图像。离焦模糊图像的退化模型可用圆盘函数描述，利用模糊图像无方向性的二阶拉氏微分图像的自相关的负相关峰形成的环形槽的直径等于作为圆盘函数直径的2倍可以确定该函数。对模糊图像进行一次维纳滤波方法得到原图像的估计值，然后利用该初始值求得原图像及噪声的谱密度估值，进而利用这些新获得的信息构成改进的维纳滤波器对退化图像进行第二次滤波。实验表明，该方法计算量小、鉴别精度高、抗噪声能力较强，突出原图像的一些关键细节，提高了图像的复原质量。     

运用BF-HHT的转子径向碰摩故障特征提取方法

针对现有希尔伯特-黄变换(HHT)方法在进行特征提取时,估算部分多频固有模式函数(IMF)瞬时频率不准确的问题,提出了一种基于带通滤波的希尔伯特-黄变换算法(BF-HHT)。该算法首先确定信号主要能量所在频率;然后以这组主要能量所在频率为中心频率设计一组带通滤波器,对原信号进行滤波;最后用HHT方法提取信号的时频特征。转子试验台和实际机组的径向碰摩故障信号的试验与分析结果表明,BF-HHT算法不仅能准确提取到转子径向碰摩故障信号中出现的频率成分,而且能对故障特征频率出现或消失的时刻精确定位,其特征提取效果明显优于HHT方法。     

Deconvolution improves colocalization analysis of multiple fluorochromes in 3D confocal data sets more than filtering techniques

Background and noise impair image quality by affecting resolution and obscuring image detail in the low intensity range. Because background levels in unprocessed confocal images are frequently at about 30% maximum intensity, colocalization analysis, a typical segmentation process, is limited to high intensity signal and prone to noise‐induced, false‐positive events. This makes suppression or removal of background crucial for this kind of image analysis. This paper examines the effects of median filtering and deconvolution, two image‐processing techniques enhancing the signal‐to‐noise ratio (SNR), on the results of colocalization analysis in confocal data sets of biological specimens. The data show that median filtering can improve the SNR by a factor of 2. The technique eliminates noise‐induced colocalization events successfully. However, because filtering recovers voxel values from the local neighbourhood false‐negative (‘dissipation’ of signal intensity below threshold value) as well as false‐positive (‘fusion’ of noise with low intensity signal resulting in above threshold intensities), results can be generated. In addition, filtering involves the convolution of an image with a kernel, a procedure that inherently impairs resolution. Image restoration by deconvolution avoids both of these disadvantages. Such routines calculate a model of the object considering various parameters that impair image formation and are able to suppress background down to very low levels (< 10% maximum intensity, resulting in a SNR improved by a factor 3 as compared to raw images). This makes additional objects in the low intensity but high frequency range available to analysis. In addition, removal of noise and distortions induced by the optical system results in improved resolution, which is of critical importance in cases involving objects of near resolution size. The technique is, however, sensitive to overestimation of the background level. In conclusion, colocalization analysis will be improved by deconvolution more than by filtering. This applies especially to specimens characterized by small object size and/or low intensities.     

A statistical approach for intensity loss compensation of confocal microscopy images

In this paper, a probabilistic technique for compensation of intensity loss in confocal microscopy images is presented. For single-colour-labelled specimen, confocal microscopy images are modelled as a mixture of two Gaussian probability distribution functions, one representing the background and another corresponding to the foreground. Images are segmented into foreground and background by applying Expectation Maximization algorithm to the mixture. Final intensity compensation is carried out by scaling and shifting the original intensities with the help of parameters estimated for the foreground. Since foreground is separated to calculate the compensation parameters, the method is effective even when image structure changes from frame to frame. As intensity decay function is not used, complexity associated with estimation of the intensity decay function parameters is eliminated. In addition, images can be compensated out of order, as only information from the reference image is required for the compensation of any image. These properties make our method an ideal tool for intensity compensation of confocal microscopy images that suffer intensity loss due to absorption/scattering of light as well as photobleaching and the image can change structure from optical/temporal section-to-section due to changes in the depth of specimen or due to a live specimen. The proposed method was tested with a number of confocal microscopy image stacks and results are presented to demonstrate the effectiveness of the method.     

采用视觉特征整合的红外弱小目标检测

针对红外光学系统在复杂背景下的弱小目标检测问题,建立了基于特征整合的信息处理模型,提出了采用视觉特征整合的弱小目标检测方法。该方法首先利用视网膜神经节细胞感受野的数学模型DOG(Different-of-Gaussian)对红外图像进行初级信息处理,初步检测出弱小目标。而后,分为空域和频域两个通道进行特征提取。在空域通道,利用图像信息构造二阶微分Hessian矩阵,通过计算其直迹与行列式进行局部极值的判定,提取出含有弱小目标的结构分量特征;在频域通道,利用小波对图像频域进行二级分解,提取出含有弱小目标的高频分量特征。最后,将空域通道与频域通道的分量特征整合,提取出复杂背景下的弱小目标。实验结果表明:当虚警率为10-3时,该方法对弱小目标的平均检测概率为95.17%。基本满足红外弱小目标检测方法的稳定可靠、精度高等要求。     

基于加权最小二乘滤波和引导滤波的铸件DR图像融合

为了让两张不同灰度窗宽和窗位的铸件X射线DR图像包含的缺陷呈现在一张图像中,发展了一种基于加权最小二乘滤波和引导滤波(guided filter)的铸件X射线DR图像融合方法。选择其中一张图像作为底层图像,另一张作为细节图像。使用两种具有边缘保护性质的滤波提取细节图中包含的缺陷信息。对底层图像作加权最小二乘滤波作为框架,再用底层图像和框架做差,分离出底层图像本身包含的缺陷信息。利用引导滤波把这两类缺陷信息加权求和。最后用框架承接加权求和后的结果后得到融合后的图像。实验结果表明获得的融合结果能够显示来自不同灰度窗宽和窗位的两张DR图像的共5个缺陷或3张铸件DR图像的共7个缺陷。本文方法可应用于铸件射线无损检测。