一种异性扩散的自适应平滑滤波算法

固定尺度的高斯滤波器对图像中的所有像素使用相同的加权系数,在平滑噪声的同时也模糊了边缘信息,影响了后期的边缘定位和提取,为此提出了一种异性扩散的自适应仿高斯滤波器,通过计算当前像素与周围像素的灰度梯度,预判该像素类型（噪声点或是边缘点）,并根据像素点类型和梯度生成自适应的、各向异性的加权系数,通过多次平滑迭代实现权重的异性扩散。实验结果表明,该滤波算法具有平滑噪声的同时保留并锐化边缘效果。     

结合核方法的选择性各向异性扩散去噪算法

在低信噪比图像噪声抑制处理中,为了有效地保持图像边缘,在基于多相位分层分割算法的各向异性扩散模型的基础上,提出一个基于核方法的选择性各向异性扩散去噪算法。该算法根据图像数据的线性不可分特点,首先利用核方法把多相位分层分割算法中的数据项从线性不可分的低维空间推广到可实现线性可分的高维特征空间,在特征空间中实现图像分割;然后根据分割得到的同质区域的梯度信息改进了P-M模型中的扩散系数;最后,在同质区域中采用改进的P-M模型平滑噪声。实验结果表明,该算法无论在噪声去除还是边缘保持上都具较好的效果。     

On Using Anisotropic Diffusion for Skeleton Extraction

We present a novel and effective skeletonization algorithm for binary and gray-scale images, based on the anisotropic heat diffusion analogy. We diffuse the image in the direction normal to the feature boundaries and also allow tangential diffusion (curvature decreasing diffusion) to contribute slightly. The proposed anisotropic diffusion provides a high quality medial function in the image: it removes noise and preserves prominent curvatures of the shape along the level-sets (skeleton features). The skeleton strength map, which provides the likelihood of a point to be part of the skeleton, is defined by the mean curvature measure. Finally, thin and binary skeleton is obtained by non-maxima suppression and hysteresis thresholding of the skeleton strength map. Our method outperforms the most related and the popular methods in skeleton extraction especially in noisy conditions. Results show that the proposed approach is better at handling noise in images and preserving the skeleton features at the centerline of the shape.     

Adaptive noise-reducing anisotropic diffusion filter

In image processing and computer vision, the denoising process is an important step before several processing tasks. This paper presents a new adaptive noise-reducing anisotropic diffusion (ANRAD) method to improve the image quality, which can be considered as a modified version of a speckle-reducing anisotropic diffusion (SRAD) filter. The SRAD works very well for monochrome images with speckle noise. However, in the case of images corrupted with other types of noise, it cannot provide optimal image quality due to the inaccurate noise model. The ANRAD method introduces an automatic RGB noise model estimator in a partial differential equation system similar to the SRAD diffusion, which estimates at each iteration an upper bound of the real noise level function by fitting a lower envelope to the standard deviations of pre-segment image variances. Compared to the conventional SRAD filter, the proposed filter has the advantage of being adapted to the color noise produced by today’s CCD digital camera. The simulation results show that the ANRAD filter can reduce the noise while preserving image edges and fine details very well. Also, it is favorably compared to the fast non-local means filter, showing an improvement in the quality of the restored image. A quantitative comparison measure is given by the parameters like the mean structural similarity index and the peak signal-to-noise ratio.     

小波域上图像非线性扩散滤波

提出一个小波域上图像扩散滤波恢复新模型。主要思想是把原图像作为最精细尺度下的小波子带，根据噪声分布的特点，导出保护较大尺度下信息的泛函模型代替小波阈值除噪，对泛函求变分得：Euler-Lagrange方程。新的滤波方法能避免小波阈值除噪的伪Gibbs现象，改进了同类型非线性扩散方程滤波的效果。利用可加算子分裂(AOS)格式求非线性扩散方程的数值解。实例的数值计算说明对图像滤波和保护边缘的有效性。     

Rician noise removal from MR images using novel adapted selective non-local means filter

The reduction of rician noise from MR images without degradation of the underlying image features has attracted much attention and has a strong potential in several application domains including medical image processing. Interpretation of MR images is difficult due to their tendency to gain rician noise during acquisition. In this work, we proposed a novel selective non-local means algorithm for noise suppression of MR images while preserving the image features as much as possible. We have used morphological gradient operators that separate the image high frequency areas from smooth areas. Later, we have applied novel selective NLM filter with optimal parameter values for different frequency regions of image to remove the noise. A method of selective weight matrix is also proposed to preserve the image features against smoothing. The results of experimentation performed using proposed adapted selective filter prove the soundness of the method. We compared results with the results of many well known techniques presented in literature like NLM with optimized parameters, wavelet based de-noising and anisotropic diffusion filter and discussed the improvements achieved.     

A blending method based on partial differential equations for image denoising

In this paper we proposed a new de-noising technique based on combination of isotropic diffusion model, anisotropic diffusion (PM) model, and total variation model. The proposed model is able to be adaptive in each region depending on the information of the image. More precisely, the model performs more diffusion in the flat areas of the image, and less diffusion in the edges of the image. And so we can get rid of the noise, and preserve the edges of the image simultaneously. To verify that, we did several experiments, which showed that our algorithm is the best method for edge preserving and noise removing, compared with the isotropic diffusion, anisotropic diffusion, and total variation methods.     

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。     

Anisotropic diffusion with generalized diffusion coefficient function for defect detection in low-contrast surface images

In this paper, an anisotropic diffusion model with a generalized diffusion coefficient function is presented for defect detection in low-contrast surface images and, especially, aims at material surfaces found in liquid crystal display (LCD) manufacturing. A defect embedded in a low-contrast surface image is extremely difficult to detect, because the intensity difference between the unevenly illuminated background and the defective region is hardly observable and no clear edges are present between the defect and its surroundings.The proposed anisotropic diffusion model provides a generalized diffusion mechanism that can flexibly change the curve of the diffusion coefficient function. It adaptively carries out a smoothing process for faultless areas and performs a sharpening process for defect areas in an image. An entropy criterion is proposed as the performance measure of the diffused image and then a stochastic evolutionary computation algorithm, particle swarm optimization (PSO), is applied to automatically determine the best parameter values of the generalized diffusion coefficient function. Experimental results have shown that the proposed method can effectively and efficiently detect small defects in various low-contrast surface images.     

基于滤波器的局部自适应全变分图像去噪模型

综合利用冲击滤波器和非线性各向异性扩散滤波器对含噪图像做预处理,然后基于边缘检测函数建立反映图像局部特征的自适应权函数,构建能同时兼顾图像平滑去噪与边缘保留的局部自适应性的全变分模型,并建议用本原对偶算法快速求解。实验结果表明,同传统的全变分图像去噪模型相比,该局部自适应全变分模型在消除噪声的同时能很好地保持图像的边缘轮廓和纹理等细节特征,得到的复原图像在客观评价标准和主观视觉效果方面均有所提高。     

中值滤波与各向异性扩散相结合的医学图像滤波方法

医学图像的滤波处理,须保留具有重要诊断意义的边缘细节信息。针对Perona-Malik(PM)各向异性扩散模型遇到强噪声则失效和扩散门限参数K依靠经验选取的不足,提出了一种改进的各向异性扩散算法。将PM算法与中值滤波结合,用经过中值滤波平滑后的梯度模代替原始图像的梯度模,以控制扩散的过程。应用自适应扩散门限(当前邻域内梯度的绝对偏差中值(MAD))和迭代终止准则,提高算法鲁棒性和效率。实验分别对超声心动图、CT图像和Lena图像进行去噪处理,用峰值信噪比(PSNR)和边缘保持能力EPI作为评价标准。实验结果表明,改进算法优于PM算法和Catte-PM方法,在提高信噪比的同时保留了图像的细节信息,可以更好地满足医学图像的使用要求。     

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。     

基于各向异性双变量收缩的含噪图像融合

针对含噪图像的融合问题,基于各向异性双变量收缩提出了一种图像融合的新算法。在小波域内,对高频子带进行各向异性双变量收缩以压缩噪声,对收缩系数进行模值选大融合,对低频子带进行算术平均融合,对融合系数进行逆变换获得融合图像。对多聚焦图像及红外与可见光图像的融合结果进行主客观性能评价表明,所提出的方法可以更好地保留源图像的细节信息,并有效抑制噪声。     

A new feature-preserving nonlinear anisotropic diffusion for denoising images containing blobs and ridges

Blobs and ridges underlie many important features in biological, biometric and remote sensing images. These images are likely to be corrupted by noise, such as live cells in fluorescent biological images, ridges and valleys in fingerprints and moving targets in synthetic aperture radar and infrared images. In this paper we present a diffusion method for denoising low-signal-to-ratio images containing blob and ridge features. A commonly used denoising method makes use of edge information in an image to achieve a good balance between noise removal and feature preserving. However, if edges are partly lost to a certain extent or contaminated severely by noise, such an approach may not be able to preserve these features, leading to loss of important information. To overcome this problem, we propose a novel second-order nonlocal derivative as a robust blob and ridge detector and incorporate it into a diffusion process to form a novel feature-preserving nonlinear anisotropic diffusion model. Experiments show that the new diffusion filter outperforms many popular filters for preserving blobs and ridges, reducing noise and minimizing artifacts.     

基于各向异性逆扩散方程的指纹图像锐化去噪方法

基于逆扩散过程的启发,提出各向异性逆扩散算法用于指纹图像的锐化去噪方法,克服了退化扩散方程对大曲率边缘点的模糊效应,兼顾了去噪和保持边界这一矛盾的两个方面,尤其适合于纹理密集的指纹图像锐化.实验结果表明,本文算法对于带噪指纹图像的锐化效果明显优于以往非线性扩散处理算法.     

Enhancement of fluoroscopic images with varying contrast

A heuristic algorithm for enhancement of fluoroscopic images of varying contrast is proposed. The new technique aims at identifying a suitable type of enhancement for different locations in an image. The estimation relies on simple preliminary classification of image parts into one of the following types: uniform, sharp (with sufficient contrast), detail-containing (structure present) and unknown (for the cases where it is difficult to make a decision). Different smoothing techniques are applied locally in the different types of image parts. For those parts that are classified as detail-containing, probable object boundaries are identified and local sharpening is carried out to increase the contrast at these places. The adopted approach attempts to improve the quality of an image by reducing available noise and simultaneously increasing the contrast at probable object boundaries without increasing the overall dynamic range. In addition, it allows noise to be cleaned, that at some locations is stronger than the fine structure at other locations, whilst preserving the details.     

梯度矢量扩散控制实现边缘保持的彩色图像去噪

目的 在图像的获取过程中,由于受到噪声等因素影响,可能导致图像质量下降,给后期处理带来困难,为此提出一种梯度矢量扩散控制实现边缘保持的彩色图像去噪方法。方法 首先分析了彩色图像因灰度化带来的信息损失,为了更好地利用彩色图像信息,构造了RGB空间下的梯度矢量计算方法。其次分析了噪声邻域梯度矢量与边缘邻域梯度矢量间区域性结构差异,并指出现有扩散方程的不足,给出一种控制扩散矩阵的获取方法。最后通过推导矢量图像的边缘函数,给出了RGB空间下的PM方程,分解该模型去除法向扩散,并结合控制扩散矩阵改进边缘停止函数,以此获得更好的矢量扩散控制。结果 实验结果表明,采用这种方法得到的处理结果有着较高的信噪比,验证了该方法的有效性。结论 本文方法能够在去噪的同时,较好地保持图像对比度与边缘信息,具备一定的实用性。     

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.     

自适应全变分图像去噪模型及其快速求解*

在联合冲击滤波器和非线性各向异性扩散滤波器对含噪图像做预处理的基础上,利用边缘检测算子选取自适应参数,构建能同时兼顾图像平滑去噪与边缘保留的自适应全变分模型,并基于Bregman迭代正则化方法设计了其快速迭代求解算法.实验结果表明,自适应去噪模型及其求解算法在快速去除噪声的同时保留了图像的边缘轮廓和纹理等细节信息,得到的复原图像在客观评价标准和主观视觉效果方面均有所提高.     

基于邻域相关信息的X射线图像增强算法

射线图像存在对比度差、灰度范围小、影像模糊等缺陷。传统的反锐化增强算法是图像增强经常用到的方法,该方法简单易行,但对噪声非常敏感。一般是加上一个低通滤波器来抑制噪声,但加上低通滤波器后又会对边缘造成模糊。 针对上述问题,提出了基于邻域相关信息的射线图像增强算法。首先对射线图像进行多幅叠加降噪,然后根据掩模中心像素和周围邻域像素灰度值的差与输出灰度的查找表进行图像增强。该方法在增强图像的同时能够很好地抑制噪声的增加,实验证明这种方法是可行的。