一种改进的PDE图像去噪方法

研究用于图像去噪的偏微分方程;在理论上对去噪原理进行了分析。通过对扩散方程中扩散系数的改进,提出了一个对噪声图像更有效、更具有适应性的去噪扩散模型,对高斯噪声图像进行处理。与传统的各向异性扩散算法进行了比较并对偏微分方程的未来发展方向进行了展望。实验结果表明,该方法在有效去除噪声的同时较好地保留了图像中的重要细节信息,使图像的细节部分清晰。该方法可以有效地去除图像噪声,提高图像的质量。     

基于异性扩散-中值滤波的超声医学图像去噪方法

针对超声图像存在一种特殊的斑点噪声,使图像边界与细节变得模糊而严重影响图像质量的问题,提出了一种新的去除医学图像斑点噪声的方法,它利用中值滤波和各向异性扩散相结合,不仅可以有效地去除噪声而且很好地保持了边缘、局部细节信息.此外,该方法在扩散过程中,梯度阈值选取的不同对图像结果影响很小,这极大地提高了该算法的健壮性.实验中,通过和各向异性扩散、中值滤波等方法的比较,表明该方法具有良好的去噪效果.     

基于平稳小波域的各向异性扩散图像去噪方法

针对传统的各向异性扩散去噪方法存在的导致图像细节丢失的问题,提出了一种基于平稳小波域的各向异性扩散图像去噪方法。该方法根据平稳小波变换的特性,通过在高频和低频子带上选用不同的梯度门限进行各向异性扩散,然后进行重构得到去噪后的结果图像。实验结果表明,该方法在有效去除噪声的同时,图像细节保留较好,去噪后的图像具有更好的质量。     

耦合冲击滤波器的片相似性各向异性扩散模型

在图像去噪过程中,为保持图像边缘并去除噪声,提出一种结合片相似性各向异性扩散(AD)和冲击滤波器的图像去噪和增强模型。采用片相似性AD模型去除图像中的噪声,引入冲击滤波器增强图像的重要结构特征。构造关于图像梯度模的函数,并自适应地调节图像在同质区域、细节和边缘区域的增强系数,在增强图像细节的同时,抑制噪声的放大和过冲现象。实验结果表明,该模型在视觉效果和客观评价指标方面均优于传统的AD模型、片相似性AD模型、结合冲击滤波器的AD模型,不仅能有效地去除噪声,且更好地保留了图像的细节和边缘特征。     

基于改进各向异性扩散的超声医学图像滤波方法

为了能有效地去除超声医学图像中的噪声,又能较好地保持图像的边缘和重要细节信息,在η-ξ正交坐标系下研究并分析了各向异性扩散模型(P-M模型)的扩散滤波机制,并在此坐标系下建立了一种新的各向异性扩散滤波方案。实验结果表明,改进的扩散模型不仅能够有效地保持图像边缘,而且还能够克服P-M模型对小尺寸噪声的敏感问题。     

医用超声图像有效去噪方法研究与仿真

研究医用超声图像的去噪问题,保证去噪有效性.超声图像成像过程中,超声波到达器官组织散射回波信号会在超声图像中产生乘性斑点噪声,对图像质量造成破坏,传统的维纳滤波方法不能有效去除乘性斑点噪声保留图像细节信息,使超声图像去噪有效性不高.为解决上述问题,提出阿尔法超声图像去噪方法,首先将超声图像进行分析和小波分解,发现超声图像的信号具有明显的非高斯特性且二维小波系数服从阿尔法分布,然后据阿尔法分布特点采用最小平均绝对误差准则的估计器去除超声图像中的噪声.仿真结果表明,改进方法能够有效去除超声图像中的乘性斑点噪声,且最大限度保留图像细节信息,保证超声图像去噪的有效性.     

量子衍生PDE医学超声图像去斑

目的 医学超声图像常常受到斑点噪声的污染而导致质量降低,影响后续诊疗.为了解决医学超声图像在滤波去斑的同时保持图像边缘细节和结构特征的问题,借鉴量子力学的基础理论,提出一种量子衍生偏微分方程(PDE)医学超声图像去斑方法.方法 针对传统P-M方程各向异性扩散的自适应去斑能力有限的问题,引入量子理论改进扩散系数增强去斑算法的自适应能力.同时构造出各向异性扩散模型,提出一种量子衍生的偏微分方程医学超声图像去斑方法.结果 通过对模拟斑点噪声污染的图像和真实医学超声图像实验,比较信噪比(SNR)、边缘保持度、结构相似度(SSIM)等客观评价指标,本文方法较其他图像去斑方法更能有效去除斑点噪声,同时又能较好地保持图像边缘细节与结构特征.结论 本文方法能够有效地解决医学超声图像去斑中保持图像细节特征的问题,同时,量子理论的引入也为后续医学超声图像的研究提供了新思路.     

鲁棒的各向异性扩散三维超声图像去噪算法

针对三维超声图像去噪,提出一种新的各向异性扩散滤波算法。该算法主要通过改进传统算法中的扩散系数以及瞬时变化系数(ICOV),在保留三维超声图像边缘和细节的同时,更好地滤除了斑点噪声。与传统算法相比,该算法对迭代次数有更低的敏感度和更好的鲁棒性。     

基于对数压缩的超声各向异性扩散去噪方法

针对当前超声图像去噪算法很难同时做到降噪和边缘保持的情况,在进行各向异性扩散模型研究的基础上,提出基于对数压缩的改进各向异性扩散算法（LCAD）去除超声散斑噪声。算法将图像对数压缩后进行噪声分布模型估计,然后构造基于广义伽马分布的扩散系数,在扩散过程中达到降噪和边缘保持效果。     

结合自适应保真项的各向异性扩散模型

为了在去除噪声的同时,对图像更好的保真,在各向异性扩散模型的基础上,提出了结合自适应保真项的各向异性扩散模型。该模型能够很好地抑制边缘上的噪声和强噪声。实验结果表明,该模型不仅能有效去除噪声,而且对图像细节、边缘也能很好的保真。     

引入欧氏距离的各向异性扩散相干斑抑制

目的 相干斑噪声严重影响SAR影像解译。抑制相干斑同时,获取较好的边缘保持效果始终是相干斑抑制的重点。针对该问题,提出一种引入欧氏距离的各向异性扩散（EDAD）相干斑抑制方法。方法 EDAD算法以P-M模型与SRAD算法为基础,利用邻近像素间区域欧氏距离代替原有边缘检测算子,自适应区分同质区与异质区,有效构造各向异性扩散系数,完成相干斑抑制。结果 运用EDAD算法与现存各向异性扩散算法对截取的两景TanDEM-X影像进行试验研究并比较各类算法的评估参数。EDAD算法的等效视数分别为3.996与5.859,均高于其他算法,体现优越的相干斑抑制能力;EDAD算法相干斑抑制前后比值影像的均值分别为0.999与1.001,方差分别为0.270与0.269,较其他算法均更接近理想值1与0.273,展现更优边缘保持与相干斑抑制能力。结论 本文算法可有效提高边缘检测能力,获取更优相干斑抑制效果。经验证,对分布较散的弱相干斑区域与分布较集中的强相干斑区域均有较好适用性。     

Ultrasound speckle reduction by a SUSAN-controlled anisotropic diffusion method

An ultrasound speckle reduction method is proposed in this paper. The filter, which enhances the power of anisotropic diffusion with the Smallest Univalue Segment Assimilating Nucleus (SUSAN) edge detector, is referred to as the SUSAN-controlled anisotropic diffusion (SUSAN_AD). The SUSAN edge detector finds image features by using local information from a pseudo-global perspective. Thanks to the noise insensitivity and structure preservation properties of SUSAN, a better control can be provided to the subsequent diffusion process. To enhance the adaptability of the SUSAN_AD, the parameters of the SUSAN edge detector are calculated based on the statistics of a fully formed speckle (FFS) region. Different FFS estimation schemes are proposed for envelope-detected speckle images and log-compressed ultrasonic images. Adaptive diffusion threshold estimation and automatic diffusion termination criterion are employed to enhance the robustness of the method. Both synthetic and real ultrasound images are used to evaluate the proposed method. The performance of the SUSAN_AD is compared with four other existing speckle reduction methods. It is shown that the proposed method is superior to other methods in both noise reduction and detail preservation.     

Edge Detection of Ultrasonic Medical Image Based on Mathematical Morphology

During the ultrasonic medical image edge detection, one of the difficult problems is that there am some speckles that show strongly variance of image gray level, the speckles reduce the space difference and remain the detailed structure under cover.Edge detection algorithms have had fairly limited application in ultrasound imaging. High levels of speckling present in ultrasound images make accurate edge detections difficult. In this paper, a novel edge detection method based on mathematical morphology is presented. Firstly, according to the statistical characteristic of the speckle, the speckle filtering method based on multidirectional morphological structure is presented. The experiment shows that the speckle noise has been filtered and image details have been preserved by the method based on local statistical property of the image, the edge detection result shows that this method is reliable and effective. Certainly the speckle filtering and the edge detection are not the final objective and would like to be proved and perfected in the further research of ultrasonic image analyzing.     

基于各向异性扩散的图像平滑及在三维重构预处理中的应用

采用基于各向异性扩散的偏微分方程，其初始值为输入图像，转化为差分格式迭代求解滤波结果。在去除噪声的同时，保持重要的边缘和局部细节。在此基础上提出了8向的各向异性扩散和边缘增强的处理技术，取得了满意的结果，并将此切片图像经聚类分群运用到三维重构中，使重构的效果更好。     

应用ROF模型的医学超声图像去噪方法

由于超声成像机制使医学超声图像中存在着大量的斑点噪声,这些斑点噪声大大降低了图像的清晰度和质量,给超声诊断带来很大的困难。针对医学超声图像的斑点噪声去噪问题,提出了一种基于帧相关处理、ROF分解和自适应小波阈值的去噪方法,能够在抑制超声图像斑点噪声的同时,尽可能地保留甚至增强图像的细节信息,大大提高图像质量,取得了很好的效果。     

改进权值非局部均值超声图像去噪

目的 超声图像斑点噪声会影响诊断的准确性和可靠性。通过分析超声图像斑点噪声统计模型,结合非局部均值滤波算法,提出一种基于超声斑点噪声模型的改进权值非局部均值（NLM）滤波算法。方法 算法针对超声图像灰度信息对图像进行预处理,利用超声图像斑点噪声模型改进传统NLM算法的权值计算函数,基于图像特征确定最优采样间隔进行下采样,利用改进后的权值计算函数对图像进行NLM去噪处理。结果 分别采用人工合成与真实超声图像对本文算法性能进行测试,并与传统非局部均值滤波算法、非局部总变分（NLTV）等算法进行去噪效果比较,同时采用均方误差、峰值信噪比和平均结构相似性作为滤波算法性能的客观评价指标。本文算法能快速完成超声图像的去噪处理,峰值信噪比较其他算法可以提高0.2 dB以上,可以降低均方误差,提高平均结构相似性,缩短处理时间,并得到较好的图像质量和视觉效果。结论 根据超声图像斑点噪声模型对NLM算法的权值计算函数进行优化,使得NLM图像滤波算法能更好地适用于超声图像的去噪,基于超声斑点噪声模型的改进权值NLM算法相较于其他算法,滤波效果更佳,适合超声图像去噪。     

Speckle reduction of ultrasound images based on Rayleigh-trimmed anisotropic diffusion filter

A novel method is proposed to reduce speckle in ultrasound images. Based on the assumption of Rayleigh distribution of speckle, a Rayleigh-trimmed filter is first proposed to estimate the relative standard deviations of local signals and the results are used to determine the parameter that controls an alpha-trimmed mean filter for suppressing the primary noise. Then the anisotropic diffusion is subsequently applied to further reduce noise while enhancing features and structures in the original image. We also extend the proposed method to three-dimensional space by introducing time as one additional dimension. The proposed method effectively utilizes the statistical characteristics of speckle and the two-step despeckling algorithm reduces speckle significantly while retaining important features. The effectiveness of the proposed method is well demonstrated by experiments on both simulated and real ultrasound images.     

A diffusion stick method for speckle suppression in ultrasonic images

This paper presents a diffusion stick method for speckle suppression in ultrasonic images. An asymmetric stick filter kernel is firstly defined by decomposing the rectangle neighborhood into line segments of variable orientations. Then, the weighted sum of averages along each stick is used to produce the filtered images. Implemented in an iteration scheme, our method works as a pseudo-diffusion process, where the diffusivity is controlled by a normalized variance function. Experiments of synthetic and real images show that the diffusion stick technique performs effectively in suppressing speckle noise, preserving resolvable structures and enhancing linear features.     

An improved speckle-reduction algorithm for SAR images based on anisotropic diffusion

This paper mainly studies the algorithm of anisotropic diffusion for speckle noise removal of SAR images. Because the Gauss curvature driven diffusion method is sensitive to the noise and is of low efficiency on suppressing the speckle noise, an improved denoising algorithm is proposed. The new algorithm introduces the difference curvature as the diffusion coefficients of the function, which solves the problem that Gauss curvature driven diffusion is sensitive to the speckle noise, further, Tukey’s biweight function is used to control the curvature diffusion model, which can not only better protect edges, but also automatically control the diffusion. Numerical experiments show that the improved algorithm can preserve the information of textures, edges while inhibiting the speckle of SAR images.