基于方向滤波器消除遥感图像孤立条带噪声的方法

条带噪声是影响遥感图像质量的重要因素之一.在分析遥感CCD图像孤立条带噪声产生原因和噪声模型的基础上,提出了基于二维方向滤波器抑制条带噪声的新方法.该方法利用了窄带方向滤波器良好的图像纹理方向的频率选择特性,将条带噪声与图像其它信息分离到不同的方向滤波器子带内,并在水平信息子带内采用均值补偿方法去除条带噪声.实验结果证明它不仅能有效去除CCD图像中的随机孤立条带噪声,还能保持原始图像的辐射入射幅度,保留细节信息,较传统的方法相比,性能有较大的提高.     

EMI噪声分析及EMI滤波器的设计

介绍了开关电源的噪声形成原因,分析了差模干扰噪声及共模干扰噪声的干扰途径,指出了减小差模干扰噪声及共模干扰噪声的具体方法,同时说明了EMI电源滤波器的工作原理,最后给出了国内外对干扰噪声所采用的一些规范和标准。     

激光遥感图像乘性噪声降噪技术

激光遥感图像乘性噪声的降噪处理中,传统解决乘性噪声的图像降噪技术存在较多的缺陷,故根据乘性噪声的非线性和时变性,提出基于TV模型的激光遥感图像乘性噪声综合降噪方法,在不破坏图像基本结构的情况下进行降噪。该方法使用TV模型针对乘性噪声的非线性和时变性进行能量变分,获取乘性噪声能量极限,综合使用规则化方法和边界约束法对能量极限进行迭代求解,从激光遥感图像的边缘处开始向内进行乘性噪声平滑降噪,很好地保留了图像边缘信息。实验对不同降噪方法进行了对比分析,结果显示,所提降噪方法具备良好的降噪性能。     

基于噪声估计的自适应开关型中值滤波器

提出了一种基于噪声估计的自适应开关型中值滤波器(IASMNE,improved adaptive switching median filter based on noise estimation)。IASMNE以图像经小波变换后在不同尺度和不同方向提取的子带滤波系数值的统计信息构成刻画图像受噪声干扰程度的特征矢量,在大量噪声图像上获得的特征矢量为学习数据集,并利用支持向量回归(SVR)分析实现对图像中噪声比例的准确估计。基于此,IASMNE对高、中、低不同噪声比例图像启动不同的滤波策略,并灵活设置滤波参数。大量实验表明,与其它开关型滤波器相比,IASMNE能够合理地根据图像噪声干扰程度进行最佳滤波,尤其是对于大于70%的椒盐噪声(SPN)能够大幅度提高图像质量。     

一个解析的适用于短沟SOIMOSFET's的高频噪声模型

提出了一个新的解析的适用于SOI MOSFET's的高频噪声模型.该模型通过耦合能量平衡方程克服了以往噪声模型所具有的缺点,并对短沟SOI器件的噪声给出精确地描述.同时,利用该模型可以容易地计算出相对于最小噪声值处的优化的栅源电压,为低噪声的电路设计提供优化的设计方向.由于该噪声模型的简单性,可以很方便地将模型植入电路模拟器如SPICE中完成电路设计.     

基于图像局部曲面可展化的混合噪声抑制算法

为满足基于旋翼无人机(UAV)载具的室外目标检测所需的低资源开销混合噪声抑制,该文提出一种基于图像局部曲面可展化的混合噪声抑制算法(DLS),该算法实现了局部曲面可展化算法和分层降噪算法优势互补,达到了两算法各自无法企及的降噪效果。首先,对图像进行局部可展化处理,抑制图像的椒盐噪声和低密度高斯噪声,得到初步降噪图像;接着,在空间域和傅里叶域分层降噪,在去除高斯噪声残余的同时,最大限度地保留图像边缘、纹理等细节;最后,迭代局部曲面可展化和分层降噪,进一步去除混合噪声残余成分,达到抑制目标检测图像混合噪声的目的。实验结果表明,在去除图像混合噪声时,相比于其他7种降噪算法,本文算法具有一定的优势,其降噪图像的主观视觉指标和客观数据指标统计优于其他7种算法。     

基于图像局部曲面可展化的混合噪声抑制算法

为满足基于旋翼无人机(UAV)载具的室外目标检测所需的低资源开销混合噪声抑制,该文提出一种基于图像局部曲面可展化的混合噪声抑制算法(DLS),该算法实现了局部曲面可展化算法和分层降噪算法优势互补,达到了两算法各自无法企及的降噪效果.首先,对图像进行局部可展化处理,抑制图像的椒盐噪声和低密度高斯噪声,得到初步降噪图像;接着,在空间域和傅里叶域分层降噪,在去除高斯噪声残余的同时,最大限度地保留图像边缘、纹理等细节;最后,迭代局部曲面可展化和分层降噪,进一步去除混合噪声残余成分,达到抑制目标检测图像混合噪声的目的.实验结果表明,在去除图像混合噪声时,相比于其他7种降噪算法,本文算法具有一定的优势,其降噪图像的主观视觉指标和客观数据指标统计优于其他7种算法.     

对数域的光学相干层析图像噪声模型分析

为了分析光学相干层析成像技术(OCT)的图像对数变换前后的斑点噪声分布模型,为图像去噪及其它图像处理过程提供参考,采用了先推导斑点噪声理论分布模型,再通过实验数据验证的方法。首先分析了对数变换前斑点噪声的统计特性,利用数学理论推导得到斑点噪声对数变换后的理论分布模型,然后用OCT心血管图像的平滑区域作为噪声的试验数据,得到变换前后斑点噪声的直方图分布数据,最后用理论分布模型对试验数据进行曲线拟合,取得了确定系数、均方根差和卡方检测的评价数据。结果表明,对数变换后斑点噪声分布符合Fisher-Tippett分布。这一结果对OCT图像去噪等处理过程是有帮助的。     

基于双边滤波器的红外图像条纹噪声消除算法

提出了一种新的消除红外图像中条纹噪声的方法。由于红外焦平面阵列中每列读出电路的偏置电压不同,导致红外图像中有明显的条纹状噪声。分析了读出电路偏置电压不均匀对红外成像的影响,提出了利用双边滤波器估计读出电路偏置电压的方法,并使用估计的偏置电压校正红外图像。基于实际红外图像的实验结果表明,提出的算法能够显著地消除红外图像中的条纹噪声。     

基于中值滤波器的红外图像噪声处理的改进方法

基于红外图像成像的机理和热像仪工作方式,红外图像往往混有大量随机噪声,而这些都是造成红外图像和视频质量下降的重要原因。中值滤波是一种常用的非线性的滤波方式,对于图像降噪有很好的效果。中值滤波器的处理窗口大小需要提前设定且在处理过程中不能改变。噪声密度越大需要处理窗口越大,但也导致图像的细节相应越模糊。综合窗口大小对降噪能力和细节处理能力的影响,文中对传统的中值滤波器算法进行改进。实验表明,在中值滤波器去除噪声的过程中,随着窗口图像噪声分布情况动态调整窗口大小,能够做到既尽可能去除噪声,又尽可能保持图片的细节,使图像处理整体效果得到提升。     

An adaptive morphological filter for image processing

Novel types of opening operator (NOP) and closing operator (NCP) are proposed. An adaptive morphological filter is then constructed on the basis of the NOP and NCP. The filter can remove any details consisting of fewer pixels than a given number N, while preserving the other details. Efficient algorithms are also developed for the implementation of the NOP and NCP.     

Nonuniform filter bank design with noises

This work solves the signal reconstruction problem involving nonuniform filter bank systems with rational decimation factors and noise. Three main nonuniform filter bank systems, i.e., filter-block decimator (FBD) structure, upsampler-filter-downsampler (UFD) structure, and tree structure, are included in this study. According to different operating conditions, two different signal reconstruction problems for nonuniform filter bank systems with noise under the unknown but identifiable input signal model and the unknown input signal model are discussed, respectively. At the first stage, a unified block state space model for different nonuniform filter bank systems with noise is developed. Then, by incorporating the identified input signal model with this unified state space model and appropriate choice of the augmented state vector, the signal reconstruction problem is reduced to an equivalent state estimation problem for resulting augmented systems if the input signal is identifiable. If the input signal is lacking in modeling, the signal reconstruction is discussed from the minimax estimation point of view. Two state estimation techniques involving robust Kalman filtering and H_∞ filtering are employed, respectively, to treat the signal reconstruction problem of nonuniform filter bank systems according to different a priori knowledge of the input signal. Finally, several numerical examples are presented to illustrate the proposed algorithms and exhibit the performances     

元胞自动机在图像高密度椒盐噪声去噪中的应用

为了提高对高密度椒盐噪声的滤波效果,针对图像椒盐噪声的特点,本文提出了一种基于元胞自动机的去噪算法。元胞自动机最核心的是状态转移函数,本算法采用Moore邻域的形式设计了一种新的状态转移函数。我们把这种算法对图像处理的结果和经典的中值滤波等处理的结果的图像和它们的PSNR值进行比较,结果表明本文提出的算法对椒盐噪声,尤其对高密度椒盐噪声的滤波,在视觉和客观指标上都有更好的去噪效果。     

A sparsity-ranking edge-preservation filter for removal of high-density impulse noises

In this study, a novel sparsity-ranking edge-preservation filter (SREPF) is proposed for removal of high-density impulse noise in images. Using the sparse matrix representation, the first stage of SREPF is not only to identify the noisy candidates but also to decide the processing order of them via a rank of noise-pixel sparsity in the working window. Then the second stage of SREPF utilizes a modified double Laplacian convolution to confirm the truly noisy pixels and yield a directional mean to recover them. This new approach has achieved more remarkable success rate of the edge detection than other edge-preservation methods especially in high noise ratio over 0.5. As a result, SREPF has significant improvements in terms of edge preservation and noise suppression exhibited by the peak signal-to-noise ratio (PSNR) and the structural similarity index metric (SSIM). Simulation results show that this method is capable of producing better performance compared to several representative filters.     

Decision-directed nonlinear filter for image processing

A complete quadratic Volterra filter working under the control of a decision algorithm is proposed for image restoration and enhancement. A particular application in the enhancement of images having reduced luminance dynamics is considered.     

Adaptive histogram-based filter for image restoration

A novel filter for image restoration is proposed in this paper. The filter estimates histogram of original image via input image. It gets a membership function through the histogram, and the membership function contains a lot of information of original image. Then a weighted fuzzy mean filter is established based on this membership function; meanwhile, the filter adaptively adopts different filter scale according to the character divergence of image region and intensity of impulsive noise. Experimental result shows that new filter gives superior performance to conventional filters and currently used fuzzy filter.     

Tri-state median filter for image denoising

A novel nonlinear filter, called tri-state median (TSM) filter, is proposed for preserving image details while effectively suppressing impulse noise. We incorporate the standard median (SM) filter and the center weighted median (CWM) filter into a noise detection framework to determine whether a pixel is corrupted, before applying filtering unconditionally. Extensive simulation results demonstrate that the proposed filter consistently outperforms other median filters by balancing the tradeoff between noise reduction and detail preservation.     

Self-tuning measurement fusion Kalman filter with correlated measurement noises

For the multisensor system with correlated measurement noises and unknown noise sta-tistics, based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variances and cross-covariances is obtained. Further, a self-tuning weighted measurement fusion Kalman filter is presented, based on the Riccati equation. By the Dynamic Error System Analysis (DESA) method, it rigorously proved that the presented self-tuning weighted measurement fusion Kalman filter converges to the optimal weighted measurement fusion steady-state Kalman filter in a realization or with probability one, so that it has asymptotic global optimality. A simulation example for a target tracking system with 3-sensor shows that the presented self-tuning measurement fusion Kalman fuser converges to the optimal steady-state measurement fusion Kalman fuser.     

有色噪声下基于Unscented粒子滤波的语音增强方法

针对含有色噪声的语音,提出了一种基于Unscented粒子滤波的单通道语音增强方法.采用时变自回归模型(TVAR)对干净语音建模,通过Unscented粒子滤波器估计AR模型的参数并滤除有色噪声.与大多数常用的粒子滤波选择的建议分布不同,Unscented粒子滤波器采用Unscented卡尔曼滤波器生成粒子滤波的建议分布.由于在粒子的更新过程中考虑了最近的观测值,Unscented粒子滤波器能够在粒子数少于传统粒子滤波算法所需粒子数目的基础上改善估计的性能.仿真实验结果表明,在有色噪声背景下该算法具有良好的语音增强效果.