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。     

基于Mallat-Zhong离散小波变换小波的超声图像各向异性扩散抑噪方法

针对传统各向异性扩散方法在超声图像散斑噪声抑制中存在的噪声抑制不充分与边缘特征保持不足的问题,提出一种基于Mallat-Zhong离散小波变换(MZ-DWT)小波的散斑噪声抑制方法。该方法将MZ-DWT小波分析与期望值最大化(EM)算法作为图像中均匀区域与边缘区域的鉴别因子,使扩散系数能够更准确地控制扩散强度与扩散速度,从而达到充分抑制噪声和保护边缘的目的。实验结果表明,所提方法在有效抑制散斑噪声的同时,更好地保持了图像细节信息,其性能优于传统各向异性扩散方法。     

基于偏微分方程的医学超声图像去噪方法

研究了各向异性扩散方程在医学超声图像去噪中的应用。在理论上对去噪原理进行了分析,并在此基础上采用改进的针对乘性噪声的各向异性扩散算法对医学超声图像去噪,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医学超声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超声图像斑纹噪声,提高图像的质量。     

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。     

Edge preservation of impulse noise filtered images by improved anisotropic diffusion

This paper provides a robust scheme for random valued impulsive noise reduction along with edge preservation by anisotropic diffusion with improved diffusivity. The defective impulse noisy pixels are detected by Laplacian based second order pixel difference operation where these defective pixels are replaced by appropriate values with regard of the gray level of their four directional neighbors. This de-noised image undergoes the diffusion operation where diffusion coefficient function is modified to make it adaptive by incorporating local gray level variance information. The proposed modified diffusion scheme effectively restore the edges and fine details destroyed during impulse noise reduction process. The effect of proposed diffusion scheme has been studied on various images and the results are compared with some existing diffusion methods which are independently used for impulse noise reduction and edge preservation. The results shows that the prior removal of impulsive noise before the application of diffusion process is advantageous over the direct application of diffusion for removing the impulsive noise. In addition, the results of the proposed diffusion scheme are compared with some of the median filter based methods which are effectively used for impulse noise reduction without caring of edge preservation. The proposed diffusion scheme sufficiently preserves the edges without boosting of impulsive noise components on images corrupted up to 50 % of the impulsive noise density.     

小波与双边滤波的医学超声图像去噪

目的：医学超声图像中的斑点噪声降低了图像质量并且限制了超声图像自动化诊断技术的发展。针对斑点噪声问题,提出了一种新型的基于小波和双边滤波的去噪算法。方法：首先,根据医学超声图像在小波域内的统计特性,在通用小波阈值函数的基础之上,改进了小波阈值函数。其次,将无噪信号的小波系数和斑点噪声的小波系数分别建模为广义拉普拉斯分布模型和高斯分布模型,利用贝叶斯最大后验估计方法得到了新型的小波收缩算法,利用小波阈值法对小波域内的高频信号分量进行去噪。最后,对小波域内的低频信号分量进行双边滤波处理,然后利用小波逆变换便得到去噪后的图像。结果：在仿真实验中,通过与其它7种去噪算法作对比,观察峰值信噪比(PSNR)等图像质量评价指标,结果表明本文算法的去噪效果优于其他相关算法。临床超声图像的实验结果进一步验证了本文算法的去噪性能。结论：本文提出了一种新型的去噪算法,实验表明本文算法能够很好地抑制斑点噪声,并且能保留图像病灶边缘等细节。     

Kernel-based fourth-order diffusion for image noise removal

The fourth-order partial differential equations have good performance on noise smoothing and edge preservation without creating blocky effects on smooth regions. However, for low signal-to-noise ratio images, the discrimination between edges and noise is a challenging problem. A novel kernel-based fourth-order diffusion is proposed in this paper. It introduces a kernelized gradient operator in the fourth-order diffusion process, which leads to more effective noise removal capability. Experiment results show that this method outperforms several previous anisotropic diffusion methods for noise removal and edge preservation.     

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.     

彩色图像SUSAN边缘检测方法

传统的彩色图像边缘检测方法主要是基于灰度图像的,先将彩色图像转化为灰度图像,然后用灰度图像边缘检测方法检测边缘。这些方法利用彩色图像的亮度信息进行边缘检测,没有考虑其色度信息。因此部分边缘不能被检测出来。提出了一种基于CIELAB空间的SUSAN彩色图像边缘检测方法。该方法首先将彩色图像从RGB空间转换到CIELAB空间,然后用基于色差的SUSAN算子检测边缘。实验结果表明：此方法能有效地检测出彩色图像的边缘。在保留图像边缘方面,性能优于基于灰度图像的边缘检测方法。     

Improved Geometric Anisotropic Diffusion Filter for Radiography Image Enhancement

In radiography imaging, contrast, sharpness and noise there are three fundamental factors that determine the image quality. Removing noise while preserving and sharpening image contours is a complicated task particularly for images with low contrast like radiography. This paper proposes a new anisotropic diffusion method for radiography image enhancement. The proposed method is based on the integration of geometric parameters derived from the local pixel intensity distribution in a nonlinear diffusion formulation that can concurrently perform the smoothing and the sharpening operations. The main novelty of the proposed anisotropic diffusion model is the ability to combine in one process noise reduction, edge preserving and sharpening. Experimental results using both synthetic and real welding radiography images prove the efficiency of the proposed method in comparison with other anisotropic diffusion methods.     

一种基于小波域隐马尔可夫模型的SAR相干斑抑制算法

相干斑噪声是SAR图像的固有特点。对相干斑抑制的要求是在平滑噪声的同时，尽量保持原始图像的结构信息。现有的许多相干斑抑制方法各有优点和不足，没有普遍的适用性。基于图像在小波域的隐马尔可夫模型(HMMs)结构，结合SAR图像中相干斑噪声的统计特性，本文提出了一种新的小波域相干斑抑制方法。仿真及实测数据处理结果表明，该方法在有效抑制相干斑的同时，更好地保持了边缘结构。与小波域软阈值去噪方法和Lee滤波器相比较，该方法在噪声平滑及边缘保持上都取得了较大的改进，并得到了较好的视觉效果。     

PolSAR anisotropic diffusion filter with a refined similarity measure and an adaptive fidelity constraint

The presence of speckle complicates the tasks of interpreting and analysing polarimetric synthetic aperture radar (PolSAR) images. The nonlinear anisotropic diffusion (AD) method has been found to perform well in removing image noise. In this article, we propose a new AD method for PolSAR speckle reduction. An iterative refinement method is employed to measure the similarity between pixels and construct the diffusion coefficient in the iteration process, by considering both information in the original speckled image and the restored image of the last iteration. In addition, to alleviate the over-smoothing problem the conventional AD methods often encounter, an adaptive fidelity constraint is added into the diffusion equation by considering local heterogeneity information and the amount of noise. Experiments on both simulated and real PolSAR images confirm the ability of the proposed method to both suppress speckle and retain image details.     

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.     

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.     

An Anisotropic Fourth-Order Diffusion Filter for Image Noise Removal

Fourth-order nonlinear diffusion filters used for image noise removal are mainly isotropic filters. It means that the spatially varying diffusivity determined by a diffusion function is applied on the image regardless of the orientation of its local features. However, the optimal choice of parameters in the numerical solver of these filters for having a minimal distortion of the image features results in forming speckle noise on the denoised image and a very slow convergence rate especially when the noise level is moderately high. In this paper, a new fourth-order nonlinear diffusion filter is introduced, which has an anisotropic behavior on the image features. In the proposed filter, it is shown that a suitable choice for a set of diffusivity functions to unevenly control the strength of the diffusion on the directions of the level set and gradient leads to a good edge preservation capability compared to the other diffusion or regularization filters. The comparison of the results obtained by the proposed filter with those of the other second and fourth-order filters shows that the proposed method produces a noticeable improvement in the quality of denoised images evaluated subjectively and quantitatively.     

基于局部方差改进的超声图像各向异性扩散去噪算法

针对各向异性扩散算法不能有效区分强噪声和弱边缘的缺点,提出了一种基于图像局部统计特征改进的算法。该算法在对图像进行各向异性扩散去噪的过程中,使用梯度阈值找到图像中灰度变化较大的点,再通过计算局部方差和局部去心方差的差值判断该点是否为噪声点,若是噪声点则使用均值滤波处理。对仿真图像和临床超声图像的实验结果表明:与传统的各向异性扩散算法相比,改进的算法在图像去噪和特征保留的能力上得到了良好的提升。     

Spatially adaptive thresholding in wavelet domain for despeckling of ultrasound images

Ultrasound imaging is widely used for diagnostic purposes among the clinicians. A major problem concerning the ultrasound images is their inherent corruption by the multiplicative speckle noise that hampers the quality of the diagnosis, and reduces the efficiency of the algorithms for automatic image processing. In this paper, we propose a new spatially adaptive wavelet-based method in order to reduce the speckle noise from ultrasound images. A spatially adaptive threshold is introduced for denoising the coefficients of log-transformed ultrasound images. The threshold is obtained from a Bayesian maximum a posteriori estimator that is developed using a symmetric normal inverse Gaussian probability density function (PDF) as a prior for modelling the coefficients of the log-transformed reflectivity. A simple and fast method is provided to estimate the parameters of the prior PDF from the neighbouring coefficients. Extensive simulations are carried out using synthetically speckled and ultrasound images. It is shown that the proposed method outperforms several existing techniques in terms of the signal-to-noise ratio, edge preservation index and structural similarity index and visual quality, and in addition, is able to maintain the diagnostically significant details of ultrasound images.     

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.     

超声图象的自适应线边界检测方法

针对超声图象存在一种特殊的斑点噪声 ,使图象边界与细节变得模糊 ,而严重影响图象质量的问题 ,提出了一种自适应线边界检测方法 ,即以不同方向与不同大小的“窄条”来近似组织边界 ,这种“窄条”是指具有不同长度、方向各异的线段 .该方法首先将经过每一像素点的“窄条”的最大投影作为该点的灰度值 ;然后应用假设试验优化方法确定“窄条”方向 ,而“窄条”的长度由基于区域增长的局部统计特性决定 .通过引入噪声抑制与边缘保留量化参数 ,进行了仿真斑点图与组织超声图的处理实验 ,并与自适应斑点抑制滤波、直接线边界检测方法进行了比较 ,结果证明 ,该方法不但能有效地抑制斑点噪声、而且能很好地保留与增强图中的组织边界与局部细节 .     

面向遥感图像无损压缩的CCD噪声抑制方法研究

CCD在成像过程中所产生的噪声会限制星地间遥感图像数据的传输效率。因此,为有效抑制CCD噪声并提高遥感图像的无损压缩比,本文提出了一种基于图像噪声标准差估计的各向异性扩散方法。首先,结合图像同质性测度和边缘提取结果提出一种遥感图像噪声标准差的自动化估计方法。而后,通过噪声模拟的方式拟合出图像噪声标准差与经典式各向异性扩散方程传导系数中梯度阈值的线性关系。基于图像噪声标准差估计和同质性测度结果,最终提出一种可自适应性调整传导系数值和迭代次数的噪声驱动式各向异性扩散方法。本文中分别采用北京一号小卫星多光谱遥感图像和标准测试图像对所提出的噪声估计方法和噪声抑制方法进行评价。试验结果表明,对比其他各向异性扩散方法,本文所提出的方法不仅可实现更好的图像复原效果,还可有效地提高多光谱遥感图像的无损压缩比,并为今后星上数据处理系统的研究提供一种较新的思路。