Speckle suppression using recursive wavelet transforms

An effective algorithm for digital image noise smoothing using wavelet transform techniques is presented in this paper. This algorithm is more powerful when compared to other existing filtering algorithms in terms of speckle suppression for synthetic aperture radar images where the presence of speckle makes the ratio of standard deviation to mean (STM) very high. Examples show that the original STM of about 0.30 (equivalent to three-look images) can be reduced to 0.05-0.03 (equivalent to more than 100-look images), with a possible small sacrifice of losing some details and narrow edges. The quantitative analysis is carried out and compared with the results of some existing filtering algorithms including median, K nearest neighbour averaging, Lee's multiplicative and Crimmins' geometric filters, showing that imagery filtered by the wavelet transform is the smoothest.     

Smoothing of ultrasound images using a new selective average filter

Ultrasound images are strongly affected by speckle noise making visual and computational analysis of the structures more difficult. Usually, the interference caused by this kind of noise reduces the efficiency of extraction and interpretation of the structural features of interest. In order to overcome this problem, a new method of selective smoothing based on average filtering and the radiation intensity of the image pixels is proposed. The main idea of this new method is to identify the pixels belonging to the borders of the structures of interest in the image, and then apply a reduced smoothing to these pixels, whilst applying more intense smoothing to the remaining pixels. Experimental tests were conducted using synthetic ultrasound images with speckle noisy added and real ultrasound images from the female pelvic cavity. The new smoothing method is able to perform selective smoothing in the input images, enhancing the transitions between the different structures presented. The results achieved are promising, as the evaluation analysis performed shows that the developed method is more efficient in removing speckle noise from the ultrasound images compared to other current methods. This improvement is because it is able to adapt the filtering process according to the image contents, thus avoiding the loss of any relevant structural features in the input images.     

近邻感知的标签噪声过滤算法

基于k近邻的标签噪声过滤对近邻参数k的选取较敏感.针对此问题,文中提出近邻感知的标签噪声过滤算法,可有效解决二分类数据集的类内标签噪声的问题.算法分开考虑正类样本和负类样本,使分类问题中的标签噪声检测问题转化为两个单类别数据的离群点检测问题.首先通过近邻感知策略自动确定每个样本的个性化近邻参数,避免近邻参数敏感的问题.然后根据噪声因子将样本分为核心样本与非核心样本,并把非核心样本作为标签噪声候选集.最后结合候选样本的近邻标签信息,进行噪声的识别与过滤.实验表明,文中方法的噪声过滤效果和分类预测性能均较优.     

灰度图像交叉熵阈值法的三维推广

针对现有的灰度图像交叉熵阈值化方法无法有效分割含有混合噪声图像的问题,在图像三维直方图的基础上提出三维交叉熵阈值化算法,同时给出三维交叉熵阈值法的快速递推公式.实验结果表明,三维方法结合了图像中像素的灰度及其局部空间的均值和中值信息,对于含有混合噪声的图像,具有比现有交叉熵阈值化算法更好的分割效果.     

Fuzzy vector median-based surface smoothing

This paper proposes a novel approach for smoothing surfaces represented by triangular meshes. The proposed method is a two-step procedure: surface normal smoothing through fuzzy vector median (FVM) filtering followed by integration of surface normals for vertex position update based on the least square error (LSE) criteria. Median and Order Statistic-based filters are extensively used in signal processing, especially image processing, due to their ability to reject outliers and preserve features such as edges and monotonic regions. More recently, fuzzy ordering theory has been introduced to allow averaging among similarly valued samples. Fuzzy ordering theory leads naturally to the fuzzy median, which yields improved noise smoothing over traditional crisp median filters. This paper extends the fuzzy ordering concept to vector-based data and introduces the fuzzy vector median filter. The application of FVM filters to surface normal smoothing yields improved results over previously introduced normal smoothing algorithms. The improved filtering results, coupled with LSE vertex position update, produces surface smoothing that minimizes the effects of noise while simultaneously preserving detail features. The proposed method is simple to implement and relatively fast. Simulation results are presented showing the performance of the proposed method and its advantages over commonly used surface smoothing algorithms. Additionally, optimization procedures for FVM filters are derived and evaluated.     

矿区遥感图像去噪方法研究

去噪是矿区遥感图像得以有效应用的重要预处理步骤。现有的基于统计、基于域变换、基于学习等遥感图像去噪方法普遍存在细节过度平滑、纹理保持不足等问题。基于引导滤波良好的边缘保持特性,提出了迭代引导滤波方法,通过对残差信息进行引导映射,并迭代进行引导滤波及超参数收缩,增强了遥感图像边缘特征提取效果;将迭代引导滤波与传统的小波软阈值、非局部均值(NLM)滤波、三维块匹配(BM3D)滤波等去噪方法结合,有效提高了传统方法的峰值信噪比,其中NLM滤波、BM3D滤波的去噪性能提升效果最明显;将迭代引导滤波与BM3D滤波融合,通过BM3D滤波初步获取去噪图像,得到残差数据,然后采用迭代引导滤波对残差数据进行处理,在提升图像去噪效果的同时,很好地保持了图像细节特征;将迭代引导滤波与BM3D滤波融合方法用于矿区遥感图像的煤矸石场识别及滑坡区域边缘识别,取得了较好的效果。     

Channel smoothing: efficient robust smoothing of low-level signal features

In this paper, we present a new and efficient method to implement robust smoothing of low-level signal features: B-spline channel smoothing. This method consists of three steps: encoding of the signal features into channels, averaging of the channels, and decoding of the channels. We show that linear smoothing of channels is equivalent to robust smoothing of the signal features if we make use of quadratic B-splines to generate the channels. The linear decoding from B-spline channels allows the derivation of a robust error norm, which is very similar to Tukey's biweight error norm. We compare channel smoothing with three other robust smoothing techniques: nonlinear diffusion, bilateral filtering, and mean-shift filtering, both theoretically and on a 2D orientation-data smoothing task. Channel smoothing is found to be superior in four respects: it has a lower computational complexity, it is easy to implement, it chooses the global minimum error instead of the nearest local minimum, and it can also be used on nonlinear spaces, such as orientation space.     

气象遥感图像及格点场重采样插值方法

重采样插值方法是气象信息处理领域研究的问题之一。针对气象遥感图像,介绍了基于地图投影坐标转换的直接重采样插值方法和间接重采样插值方法;针对气象格点场,介绍了双线性插值方法和贝塞尔插值方法。以气象业务中不同分辨率的气象卫星（FY2E和FY2D）遥感图像以及欧洲中期天气预报中心（ECMWF）降水预报场为例,分别对不同重采样插值方法进行了分析比较。结果表明：基于间接重采样的气象遥感图像最近邻点插值法的计算量小于邻点权重插值方法,而邻点权重插值方法的效果优于最近邻点插值方法;随着图像的分辨率提高,最近邻点插值法与邻点权重插值方法相比,计算量小的优势更加明显;对于高分辨率的气象遥感图像建议采用基于间接重采样的最近邻点法;对于气象格点场,贝塞尔插值方法的插值效果优于双线性插值方法。     

基于噪声分类的双边滤波点云去噪算法

针对三维点云数据模型在去噪光顺中存在不同尺度噪声的问题,提出一种基于噪声分类的双边滤波点云去噪算法。该算法首先将噪声细分为大尺度和小尺度噪声,并使用统计滤波结合半径滤波对大尺度噪声进行去除;然后对三维点云数据进行曲率估计,并对现有点云双边滤波进行改进,增强其鲁棒性和保特征性;最后使用改进的双边滤波对小尺度噪声进行光顺,实现三维点云数据模型的去噪、光顺。与单独使用双边滤波、Fleishman双边滤波相比,改进算法在三维点云数据模型光顺平均误差指标上分别降低了50.53%和21.67%。实验结果表明,该改进算法对噪声进行尺度的细分既提高了计算效率,又避免了过光顺和细节失真,较好地保持模型中的几何特征。     

基于多尺度核函数的散乱点云数据过滤方法*

针对光滑曲面采样散乱点云含有噪声及异常数据的问题,提出了一种基于多尺度核函数的过滤处理方法。采用核密度估计技术及均值漂移跟踪算法对原始点云数据进行聚类,结合局部似然函数来测度一个三维点位于采样曲面上的概率,利用过滤后的极大似然点集精确地逼近采样曲面,最后结合经典网格化算法能够获得较好的曲面重构效果。处理实例证明,该方法实用性好,不仅能够很好地抑制不同幅值的噪声,同时也能够探测到异常数据并进行自动清除。     

An adaptive window mechanism for image smoothing

Image smoothing using adaptive windows whose shapes, sizes, and orientations vary with image structure is described. Window size is increased with decreasing gradient magnitude, and window shape and orientation are adjusted in such a way as to smooth most in the direction of least gradient. Rather than performing smoothing isotropically, smoothing is performed in preferred orientations to preserve region boundaries while reducing random noise within regions. Also, instead of performing smoothing uniformly, smoothing is performed more in homogeneous areas than in detailed areas. The proposed adaptive window mechanism is tested in the context of median, mean, and Gaussian filtering, and experimental results are presented using synthetic and real images and compared with a state-of-the-art method.     

基于矢量尺度的图像滤波

对于医学图像滤波来说,很重要的一点就是滤波后的图像应该尽可能地保留图像中的边缘和细节特征.但通常在滤波过程中,在消除噪声的同时会模糊图像中一些重要的结构信息.在最近几年中,基于尺度的滤波方法已经有效地应用在灰度图像的滤波中.现把基于尺度的方法推广到矢量(彩色)图像的滤波中.在传统滤波方法(矢量中值滤波、基本矢量方向滤波和方向距离滤波)基础上,相应地提出了3种基于球尺度的矢量滤波器.新的滤波方法能根据图像中像素的尺度信息,在图像边缘和细节附近,即区域边界执行较小的平滑,而在区域内部进行较大的平滑,从而能够自适应地控制滤波过程.实验结果表明,所提出的滤波方法与传统滤波方法相比,在消除噪声的同时更能够保留图像中的边缘和细节特征.     

点云模型的噪声分类去噪算法

针对三维点云模型数据在去噪平滑过程中存在的不同尺度噪声和算法计算耗时问题,提出了点云模型的噪声分类去噪算法。该算法根据噪声点分布特性,将其分为大尺度和小尺度噪声,先利用统计滤波结合半径滤波去除大尺度噪声;然后使用快速双边滤波对小尺度噪声进行平滑,实现点云模型的去噪和平滑。与传统的双边滤波相比,利用快速双边滤波对点云模型数据进行平滑,有效地提高了计算效率。实验结果表明,该算法对点云噪声进行快速平滑去除的同时又能有效地保持被扫描物体的几何特征。     

自适应中心加权的彩色图像中值滤波方法

提出一种彩色图像自适应中心加权的矢量中值滤波方法。通过引入投影距离确定与待滤波点最接近的若干个像素的关系,并考虑该点与由聚合距离所确定的中值的关系,自适应地确定该点的权系数,形成相应模板下的一个多重向量集合,将该集合的中值作为滤波中值。实验证明所提出方法能有效地去除彩色图像的椒盐噪声,同时能够较好地保持其色调和细节信息。     

一种新的图象平滑方法及其性能评价

本文提出了一种新的图象平滑方法--修正的邻域平均法.这种方法不仅能够有效地平滑噪声,还能够锐化模糊的图象边缘;计算比较简单,不需要任何先验知识和预定的参数.为了定量地评价这种平滑方法的性能,本文对计算机产生的试验图象定义了一种图象优值,作为评价的指标.并且通过实验将这种平滑方法的性能与梯度的倒数加权平均法、中值滤波法进行了比较.最后给出了处理实际照片的结果.     

一种新的基于噪声点检测的脉冲噪声去噪算法

中值滤波是广泛应用于去除脉冲噪声的一种非线性去噪方法,但是单一地使用中值滤波方法去除脉冲噪声会造成图像细节信息的丢失,从而使图像变得模糊。基于噪声点检测的脉冲噪声滤波方法可以在滤除噪声的同时有效地保持图像的细节信息。该文在此基础上提出了一种新的基于噪声点检测的脉冲噪声滤波算法,该算法在检测噪声点时用被检测点的中值滤波结果作为判定该点是否为噪声点的依据。而在滤除被检测到的噪声点时,采用的是迭代的中值滤波算法。从实验结果中可以看到,与其它中值滤波算法相比,该文的算法在去除脉冲噪声时能取得较好的效果。     

小波变换与分块统计在掌纹识别中的应用

用于身份鉴别的掌纹识别为信息安全提供了一种新的方案。提出一种变换域和统计域相结合的掌纹识别方法。对掌纹感兴趣区域（ROI）进行中值滤波再多级小波分解,对所有的高频子图像进行分块,求取每一子块高频系数的均值和方差,它们的组合构成该图像的特征向量,利用简单的最近邻分类器进行分类。运用UST掌纹图像库,对该算法进行了测试。从识别率为95.5%的实验结果看,该方法优于目前在掌纹识别上使用较多的子空间法。     

球面上的最近邻查询方法研究

球面上的最近邻查询在空间数据库最近邻查询领域具有重要的意义。为了处理球面上的最近邻查询问题,针对球面上数据对象点的特征和近邻查询的需要,给出了处理球面上最近邻查询的3种方法：利用球面voronoi图计算最近邻方法（VNS）;利用欧氏空间内的空间数据索引结构方法（SPINS）和降维方法（APNS）。进一步,在动态的密集数据集和动态的稀松数据集两种典型的组合情况下分别着重对3 种方法处理最近邻查询的性能进行了实验比较。理论分析和实验结果表明,给出的3种方法可较好地处理球面上具有不同性质特征的空间数据对象点的近邻查询问题。     

Finding out general tendencies in speckle noise reduction in ultrasound images

This article investigates and compiles some of the techniques mostly used in the smoothing or suppression of speckle noise in ultrasound images. With this information, a comparison of all the methods studied is done based on an experiment, using quality metrics to test their performance and show the benefits each one can contribute. To test the methods, a synthetic, noise-free image of a kidney is created and later simulations using Field II program to corrupt it are performed. This way, the smoothing techniques can be compared using numeric metrics, taking the noise-free image as a reference. Since real ultrasound images are already noise corrupted images and real noise-free images do not exist, conventional metrics cannot be used to indicate the quality obtained with filtering. Nevertheless, we propose the use of the tendencies observed in our study in real images.     

Despeckling SAR-type multiplicative noise

Despeckling multiplicative noise is important in processing coherent radar images. Assuming that measurements are corrupted by multiplicative noise and that a priori values are contaminated by either multiplicative or additive noise, we have obtained Bayesian, maximum likelihood and weighted least-squares (LS) estimators, based on gamma and normal distributions. These estimators have been shown to be biased if the noise in measurements is multiplicative. A technique of bias-correction to remove biases from the estimated parameters is proposed. The bias-correction technique requires no distributions about the measurements and the a priori mean, and can be applied to eliminate bias from Bayesian, maximum likelihood and weighted LS estimators in multiplicative noise models. It theoretically provides a solid foundation for, and thus justifies some of current practice in, despeckling multiplicative noise, such as Lee's local statistics and Kuan's adaptive smoothing filter. Some despeckling measures are also proposed and simulated experiment results are reported.