超声图像中目标的边界提取

由于对比度低和噪声强等特点,超声图像分割很难达到满意的效果,针对这一状况,将Gabor滤波器结合形态学操作应用到超声图像中目标的边界提取。首先对原始图像做预处理,去噪增强,然后再利用Gabor滤波进行目标区域特征提取,再经形态学腐蚀、膨胀等操作平滑图像,最后由sobel算子提取最终的边界图像。该法的关键之处在于Gabor滤波器尺度参数的选择,通过实验和分析得出了一组最佳尺度参数,取得了良好的分割效果,同时也验证了这一方法在超声图像分割中的实用性。     

基于灰阶熵的模糊对比度自适应图像增强算法

一般的数字图像存在噪声大、对比度低、边缘模糊等缺陷.为了有效地增强图像的模糊对比度,以满足后续的识别与检测要求,提出了一种基于灰阶熵的模糊对比度自适应图像增强算法.在模糊域内,根据邻域窗口灰阶熵值的大小,合理选取阈值,对阈值两侧的图像像素点进行不同程度的对比度增强处理,实现局部特征的增强.实验结果表明,该方法不仅增强了图像的整体对比度,而且有效地丰富了目标图像的细节信息,并抑制了噪声的放大.     

保持色调的彩色图像形态学局部对比度增强算法

提出一个针对HSV颜色空间彩色图像的形态学增强算法：利用文献[2]的颜色向量序、由此实现的膨胀和腐蚀算子,设计了一种形态学切换（toggle mapings）变换的实现算法,该算法利用膨胀和腐蚀算子处理后的图像对原始图像进行局部对比度增强,并且能够保持彩色图像的色调不发生变化。从实验结果看,算法增强图像对比度的效果良好。     

生物芯片图像样点的自动识别

样点自动识别是生物芯片信息自动提取的关键。根据样点、噪声和背景特征的关系提出一种新的自动识别方法。使用数学形态学和均值算子相结合的方法实现图像的滤波增强和背景亮度的估计；通过对功率谱的分析实现图像的倾斜校正和样点中心的网格定位；利用样点边缘亮度与均方差的特征实现样点中心和半径的校正。多幅生物芯片处理的实验证明该方法具有良好的抗噪声能力和弱信号辨识能力，能快速、准确地实现样点自动识别．     

一种新的红外图像目标检测方法

提出了一种新的海空背景下受强杂波、噪声污染的红外图像目标检测算法．算法利用了小波变换的多分辨率、多尺度特性，将要检测的图像分解到不同频率的多个尺度上，再采用形态学的背景估计和形态滤波技术，对分解后的子图像进行处理、小波重构．仿真实验表明，该算法可较强地抑制云层、海浪以及海天线的强杂波背景和强噪声的干扰，可检测出信杂比(SCR)为2的目标，适用于舰栽红外警戒系统．     

基于小波多尺度积与形态学的瑕疵检测算法

针对布匹瑕疵种类多以及单个方法仅对特定类瑕疵有效的问题,提出一种新的基于小波多尺度积和数学形态学的布匹瑕疵检测算法.首先对输入图像进行二进小波变换后,低频近似子图经过数学形态学运算,得到良好的瑕疵形状特征,然后对高频子图使用小波多尺度积方法,可以抑制噪声的同时增强瑕疵的边缘线性特征,最后融合得到最终的检测结果.实验结果...     

Contourlet变换和遗传算法相结合的沥青红外图像增强方法

沥青施工过程中,采集的红外图像容易受到周围环境噪声的影响,使图像变得模糊、信噪比低,从而导致后续图像处理分析的准确度降低。针对该噪声特性,提出了一种Contourlet变换和遗传算法相结合的红外图像增强方法。首先对原始红外图像进行Contourlet变换,得到带有多尺度、多方向信息的带通子带,然后对其进行模糊增强,并通过自适应遗传算法优化模糊增强参数,最后对增强后的带通子带进行Contourlet逆变换,得到效果增强的红外图像。实验结果表明,与其它几种常用的红外图像增强方法相比,此方法能更有效地抑制噪声,提高清晰度,取得了较好的增强效果。     

红外图像实时增强的新算法

针对红外图像的特点,提出了一种红外图像实时增强的新算法。该算法通过分析图像的直方图,得到图像中目标像素数峰值的估计值,并作为平台直方图均衡化的阈值。用该阈值对直方图进行修正,然后通过修正后的直方图进行直方图均衡化。在FPGA内通过采用并行处理结构及流水线技术实现了该算法,并且每秒可处理25帧128×128×8bits的红外图像。理论分析和实验结果均表明,本算法克服了采用一般直方图均衡化增强红外图像的缺点?对背景和噪声增强过度,抑制了目标的增强。该算法对红外图像增强后,图像对比度是直方图均衡化增强后图像对比度的1.8倍。     

基于LMD的多尺度形态学在齿轮故障诊断中的应用

数学形态学是一种非线性、非平稳分析方法,具有很强的抑制脉冲干扰的能力,但滤除白噪声的能力却不行。针对这一不足,提出一种基于LMD的多尺度形态学解调方法。该方法是将多分量调频调幅故障信号分解为一系列单分量调频调幅信号(PF),实现对故障信号的降噪处理,同时还可以获得原始信号的全部调制信息。选取能量高的PF分量求和重构,再用多尺度形态学差值滤波器提取出故障信号的频率特征。通过仿真和齿轮故障模拟实验证实了该方法的有效性。     

一种高效地修正Retinex图像自适应对比度增强算法

介绍了修正Retinex的图像对比度增强方法,引入了非线性变换函数修正彩色图像的照射分量和反射分量.全局对比度增强函数拉伸图像照射分量,改善了全局视觉效果.非线性S型函数对较大和较小的反射分量值改变较小,对中间值的改变较大,从而改善了图像局部对比度.将该方法与基于Retinex模型的图像增强方法进行了分析比较,结果表明,本算法处理后的图像具有更好的视觉效果,能够显著提高图像明亮区域和黑暗区域的可视细节.常规Retinex方法由于受到多尺度卷积运算的影响,运算复杂度普遍较高,提出的方法克服了目前常规的Retinex图像增强方法的不足,在RGB彩色空间和许多分离色度亮度的彩色空间的处理速度都很快,参数自适应较好,处理图像也没有出现明显的彩色失真现象.     

新颖的CB形态学串并复合散斑噪声滤波器

基于轮廓结构元素形态学(CB形态学)和广义多结构元素的数学形态学,构造了一种新颖的用于抑制图像中的散斑噪声的串并复合滤波器.首先采用全方位多结构元素的广义多刻度形态开最大滤波器与广义多刻度形态闭最小滤波器相并联的滤波器进行滤波,力求在滤波的同时图像几何细节损失尽可能少,然后通过级联的CB开和CB闭滤波器,再滤除剩余的斑点噪声,从而在有效抑制图像中的散斑噪声的同时更好地保持了图像的细节信息.分析和仿真表明了本文提出的滤波算法的有效性.     

Image speckle noise denoising by a multi-layer fusion enhancement method based on block matching and 3D filtering

In order to improve speckle noise denoising of block matching and 3D filtering (BM3D) method, an image frequency-domain multi-layer fusion enhancement method (MLFE-BM3D) based on nonsubsampled contourlet transform (NSCT) has been proposed. The method designs an NSCT hard threshold denoising enhancement to preprocess the image, then uses fusion enhancement in NSCT domain to fuse the preliminary estimation results of images before and after the NSCT hard threshold denoising, finally, BM3D denoising is carried out with the fused image to obtain the final denoising result. Experiments on natural images and medical ultrasound images show that MLFE-BM3D method can achieve better visual effects than BM3D method, the peak signal to noise ratio (PSNR) of the denoised image is increased by 0.5?dB. The MLFE-BM3D method can improve the denoising effect of speckle noise in the texture region, and still maintain a good denoising effect in the smooth region of the image.     

阵列信号维纳滤波用于主动声呐图像增强处理

声呐图像的噪声背景抑制是提高水下目标检测能力的重要问题。针对声呐图像背景斑点噪声强、目标轮廓模糊、目标回波对比度低等问题,利用确定性目标回波信号与随机分布的干扰噪声之间的相关统计特性差异,采用基于最小均方差准则的阵列信号维纳滤波器,通过主动最小方差无畸变响应（Minimum Variance Distortionless Response,MVDR）波束形成和后置维纳滤波的两级处理,去除声呐随机噪声背景。试验数据的处理结果表明：在噪声干扰条件下,相比于常规波束形成（Common Beamforming,CBF）,主动MVDR处理提高了目标回波的局部信噪比,后置维纳滤波处理降低了随机分布的斑点噪声,使声呐图像的清晰度得到增强。     

基于自适应形态滤波的医学超声图像降噪

针对医学超声图像上的斑点噪声,本文提出一种基于自适应形态滤波的降噪方法.首先构造一组检测图像中不同像素值突变的结构因子;再对每个结构因子构造相应的形态滤波结构元;最后对每个像素点邻域进行结构检测,找到该点处最可能存在的突变结构,以相应的结构元完成该点的形态滤波.对不同信噪比的仿真图像和实际图像分别采用本文方法和各向异性扩散滤波,不同尺度传统形态滤波进行了:比较实验,结果表明:采用本方法可将超声图像的信噪比、对比度噪声比和图像优度分别平均提高15%、37%和69%,优于其它方法.     

Noise-suppressed image enhancement using multiscale top-hat selection transform through region extraction

Enhancing an image through increasing the contrast of the image is one effective way of image enhancement. To well enhance an image and suppress the produced noises in the resulting image, a multiscale top-hat selection transform-based algorithm through extracting bright and dark image regions and increasing the contrast between them is proposed. First, the multiscale top-hat selection transform is discussed and then is used to extract the bright and dark image regions of each scale. Second, the final extracted bright and dark image regions are obtained through a maximum operation on all the extracted multiscale bright and dark image regions at all scales. Finally, by using a weight strategy, the image is enhanced through increasing the contrast of the image by adding the final bright regions on and subtracting the final dark regions from the original image. The weight parameters are used to adjust the effect of image enhancement. Because the multiscale top-hat selection transform is used to effectively extract the final image regions and discriminate the possible noise regions, the image is well enhanced and some noises are suppressed. Experimental results on different types of images show that our algorithm performs well for noise-suppressed image enhancement and is useful for different applications.     

基于改进MultiResUNet网络的甲状腺超声图像分割

甲状腺超声图像分割在临床超声图像研究中有很重要的意义。针对甲状腺超声图像信噪比低,斑点噪声多,且甲状腺形态不确定等问题,提出了一种改进的MultiResUNet分割网络(称为Oct-MRU-Net网络)。该方法在MultiResUNet网络的基本结构的基础上引入Octave卷积,并采用改进的Inception模块学习不同空间尺度的特征,将训练过程中的特征图按通道方向分为高低频特征。其中,高频特征描述图像细节和边缘信息,低频特征描述图像整体轮廓信息。在甲状腺超声图像分割过程中可以重点关注高频信息,减少空间冗余,从而实现对边缘更加精细的分割。实验结果表明,Oct-MRU-Net网络的性能相较于U-Net网络和MultiResUNet网络都有较大的提升,说明该网络对甲状腺超声图像的分割效果较好。     

Ultrasound Speckle Reduction Based on Histogram Curve Matching and Region Growing

The quality of ultrasound scanning images is usually damaged by speckle noise. This paper proposes a method based on local statistics extracted from a histogram to reduce ultrasound speckle through a region growing algorithm. Unlike single statistical moment-based speckle reduction algorithms, this method adaptively smooths the speckle regions while preserving the margin and tissue structure to achieve high detectability. The criterion of a speckle region is defined by the similarity value obtained by matching the histogram of the current processing window and the reference window derived from the speckle region in advance. Then, according to the similarity value and tissue characteristics, the entire image is divided into several levels of speckle-content regions, and adaptive smoothing is performed based on these classification characteristics and the corresponding window size determined by the proposed region growing technique. Tests conducted from phantoms and in vivo images have shown very promising results after a quantitative and qualitative comparison with existing work.     

Performance evaluation of partial differential equation models in electronic speckle pattern interferometry and the delta-mollification phase map method

The ordinary differential equation (ODE) and partial differential equation (PDE) image- processing methods have been applied to reduce noise and enhance the contrast of electronic speckle pattern interferometry fringe patterns. We evaluate the performance of a few representative PDE denoising models quantitatively with two parameters called image fidelity and speckle index, and then we choose a good denoising model. Combining this denoising model with the ODE enhancement method, we make it possible to perform contrast enhancement and denoising simultaneously. Second, we introduce the delta-mollification method to smooth the unwrapped phase map. Finally, based on PDE image processing, delta mollification and some traditional techniques, an approach of phase extraction from a single fringe pattern is tested for computer-simulated and experimentally obtained fringe patterns. The method works well under a high noise level and limited visibility and can extract accurate phase values.