SAR图像乘性噪声模型分析

从SAR相干成像的物理散射机制出发,SAR乘性噪声模型认为SAR图像的每个分辨单元强度是由该单元中地物的RCS被一个强度服从单位均值(均值为1)指数分布的乘性噪声调制而成。即可以认为SAR图像是场景中地物的RCS和单位均值指数强度分布噪声的乘积。利用实测SAR图像数据库,首先证实了SAR图像中乘性噪声模型比加性噪声模型更合理。然后,在每个目标/姿态区间提取图像模板,估计每个目标切片对应的乘性噪声,对每类目标的乘性噪声分布进行直方图拟合,并采用拟合优度检验评估了拟合精度,结果表明:乘性噪声确实能用单位均值指数分布较精确描述,这也进一步证实了乘性噪声模型的正确性。最后,给出了分辨率变化(9.6m～0.3m不同)和视角变化(两种不同的视角)时单位均值指数分布对SAR乘性噪声直方图的拟合实验,结果表明单位均值指数分布对于分辨率参数和视角参数变化的情况下都是SAR乘性噪声的较精确模型。     

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。     

基于光谱空间信息的高光谱遥感图像混合噪声评估

综合利用高光谱图像的光谱信息和空间信息,提出了一种新的混合噪声评估方法.首先通过滤波算法进行图像中均匀图像块的自动选取;然后利用多元线性回归模型,将均匀图像块内像素点的信号值和噪声值进行分离,并实现了图像中加性、乘性噪声的粗评估;最后根据噪声模型构建似然函数,利用最大似然估计法求解噪声模型参数.通过仿真图像和真实高光谱图像进行实验,验证了该方法的准确性和鲁棒性.     

高重频距离选通水下激光雷达图像噪声特性分析

高重频距离选通水下激光雷达采用多脉冲累积方式成像。分析高重频距离选通水下激光雷达图像噪声特性,是进一步采用图像后期处理方式提高成像质量的必要工作。通过对加性噪声和乘性噪声特性进行分析,对单脉冲距离选通水下激光雷达图像、高重频距离选通水下激光雷达图像和经多帧平均后的高重频距离选通水下激光雷达图像的噪声特性进行了分析。实验结果表明,单脉冲距离选通水下激光雷达图像主要受到乘性噪声的影响,高重频距离选通水下激光雷达图像受到加性噪声和乘性噪声两种噪声的双重影响,并且当对高重频距离选通水下激光雷达图像做多帧累加平均后,图像主要含有乘性噪声。     

一种改进的非凸正则项模型的噪声去除方法

针对非凸正则项模型,在去除乘性噪声时边缘信息对噪声敏感且强度较大的噪声抑制能力弱的缺陷,提出了一种改进的图像去噪新模型。在新模型中通过取对数将乘性噪声转变成加性噪声,然后在模型的正则项和忠诚项中均引入高斯卷积,既对图像进行平滑预处理,又获得丰富的边缘信息,从而对边缘作出精确定位,使新模型具有良好的鲁棒性并根据图像的特征进行平滑,因而更好地保护了图像的边缘。数值实验表明,新方法的去噪结果在定量指标上有大幅提高,视觉效果上也有较大改善,尤其是对强度较大的噪声,新方法的优势更突出。     

基于改进阈值函数的SAR图像小波去噪方法

为了抑制SAR图像的相干斑噪声,对Garrote阈值函数做了改进,增加指数函数使其更易于逼近其渐近线,提高函数中阈值参数和自变量的阶数,以缩小其偏差性.将改进的Garrote阈值函数用于SAR图像小波阈值去噪.首先,在图像预处理阶段,利用常规方法将SAR图像相干斑乘性噪声模型转换为加性噪声模型,以利于小波滤波处理;其次...     

星载分布式SAR干涉信号仿真及相干性分析

仿真是进行星载分布式合成孔径雷达(SAR)干涉测量信号处理研究的重要途径。论述了利用小面单元模型、乘性噪声模型及系统的几何模型和信号模型等模型来仿真复图像和干涉图的方法，分析了仿真复图像的相干性。通过计算机仿真，实现了仿真过程，验证了复图像的相干性。     

基于TV模型对含乘性噪声图像的去噪算法

在针对乘性噪声的TV模型的基础上,将原本针对加性噪声的典型模型引入到乘性噪声模型中去.在进行数值差分计算时,与常用的显式和半隐式差分实现方法不同,本文采用备份迭代矩阵的方法来实现显式差分,该方法可使模型在去除噪声的同时,能够保持图像的对比度不变.在此基础上,通过在正则化项中加入常数,使得模型在使用显式差分实现时稳定性大为提高,从而简化了模型实现的难度,提高了算法的速度,并且还一定程度上缓解了阶梯效应的出现.试验结果表明,改进的算法效果明显.     

Contourlet域超声图像自适应降斑算法研究

结合Contourlet系数的结构特点和超声图像相干斑乘性噪声模型,提出了一种新的基于Contourlet变换的斑纹噪声抑制算法.该算法通过计算方差一致性测度(VHM),用局部自适应窗口估计阈值萎缩因子,实现超声图像的降斑处理.实验结果表明,该算法在有效抑制斑纹噪声的同时,更有利于保持图像的边界信息,尤其适用于强噪声背景的超声图像.     

基于上下文和隐类属的小波域马尔可夫随机场SAR图像分割

该文针对合成孔径雷达(Synthetic Aperture Radar, SAR)图像含有大量的乘性斑点噪声的特点,提出了一种小波域隐类属的马尔可夫随机场(Markov Random Field, MRF)图像分割算法来抑制噪声的影响。考虑到小波的聚集性和持续性,该算法重新构造了待分图像小波域模型以类属为隐状态的混合长拖尾模型,将隐类属的马尔可夫随机场推广到小波域上,并用改进的上下文模型估计尺度间转移概率,最后推导出了新的最大后验(Maximum A Posteriori, MAP)分割公式。仿真结果证明,该算法具有鲁棒性能够有效地抑制噪声对图像的影响,得到准确的分割结果。     

Experimental study of the effect of parametric noise on the Andronov–Hopf bifurcation in brusselator

Specific features of the Andronov–Hopf bifurcation in the model of brusselator in the presence of parametric noise are numerically and experimentally studied. Regularities of evolution of the probability distribution with an increase in the noise intensity are typical of the additive and multiplicative effect of the Gaussian white noise. The existence of bifurcation interval corresponding to the gradual transition to the generation regime is experimentally revealed for both additive and multiplicative noise.     

Stochastic Resonance in Bistable System Subject to Multiplicative and Additive Noise

In this paper, a simulation model of bistable system subject to multiplicative and additive noise is built on the basis of the theory of stochastic resonance(SR). SR phenomenon appears in the system subject to multiplicative and additive noise when a single signal transmits in the system. The output waveforms and the power spectrums at different frequencies are compared. The impact of the intensity of multiplicative and additive noise on the bistable system is discussed. It is found that this simulation model can upgrade the quality of the signal processing and the noise intensity can be effectively used for improving the effect of SR.     

Improved MLV filter to remove multiplicative noise

More methods can be used to remove the additive noise, such as the Mean of Least Variance （MLV） filter. When the signal is noised by the multiplicative noise, it is difficult to remove. The paper presents an improved filter to remove multiplicative noise by changing the multiplicative noise to the additive noise, and then using the MLV-like to remove the additive noise. The simulation results show that the performance is better than Minimum Coefficient of Variation （MCV） filter and MLV filter. Both one-dimension and image experiments demonstrate its theoretical performance.     

乘性和加性噪声中二维谐波频率估计

噪声中的谐波恢复问题是信号处理领域的一个典型问题,在众多领域中有着广泛的应用。本文主要研究零均值乘性和加性噪声并存下的二维谐波信号频率估计问题,提出了一种基于数据矩阵的奇异值分解和子空间的旋转不变性的零均值乘性和加性噪声中的谐波频率的估计方法。乘性噪声为零均值情形下传统的估计方法往往难以直接应用或估计失效。本文利用谐波模型信号特征,通过对观测信号进行平方运算构造了一个数据矩阵。通过对数据矩阵的特征值进行理论分析,结合子空间旋转不变性,得到了零均值乘性和加性噪声中的谐波频率和数据矩阵之间的一种内在关系。这个性质可以用于零均值乘性和加性噪声并存下的二维谐波信号频率估计,并且所得的二维频率能自动配对。仿真实验验证了本文所提算法的有效性。      

Nonequilibrium Phenomena in Globally Coupled Active Rotators with Multiplicative and Additive Noises

We investigate noise-induced phase transitions in globally coupled active rotators with multiplicative and additive noises. In the system there are four phases, stationary one-cluster, stationary two-cluster, moving one-cluster, and moving two-cluster phases. It is shown that multiplicative noise induces a bifurcation from one-cluster phase to two-cluster phase. Pinning force also induces a bifurcation from moving phase to stationary phase suppressing the multiplicative noise effect. Additive noise reduces both effects of multiplicative noise and pinning force urging the system to the stationary one-cluster phase. The frustrated effects of pinning force and additive and multiplicative noises lead to a reentrant transition at intermediate additive noise intensity. Nature of the transition is also discussed.     

Locally optimum detection of signals in a generalized observationmodel: the known signal case

A generalized observation model for signal detection problems is proposed. The model allows consideration of several interesting special cases including additive noise, multiplicative noise, and signal-dependent noise, and includes both deterministic and random signals. Locally optimum (LO) detectors are interesting generalizations of those for the additive noise model. Under the proposed observation model the performance of the LO detectors is compared with that of other common detectors     

由统计特性分析激光主动成像系统图像的噪声性质

激光主动成像系统具有分辨率高、抗干扰能力强、能成三维像等一系列优点，因而应用越来越广泛，但它的图像同时受到散斑噪声和其他一些高斯噪声的影响。散斑噪声为一种乘性噪声，高斯噪声则为一种加性噪声，这两种噪声哪种占据主要地位对于图像噪声抑制噪声模型的建立具有重要的意义，分析了受到散斑噪声和高斯噪声污染图像的概率曲线及灰度均值方差比等统计特性，找出在两种噪声分别作用下的不同统计特性，以此为判别标准，对激光主动成像系统图像进行分析，得出了此图像所受噪声的主要成分为散斑噪声的结论。同时，算出了此图像的散斑噪声方差。     

基于领域灰度的模糊C均值图像分割算法

模糊C均值(FCM)聚类算法对图像局部灰度值不均匀和噪声十分敏感,提出一种基于像素点灰度补偿校正和邻域信息的FCM新算法.通过预先假定像素点存在加性或乘性噪声,再将像素点的邻域信息引入到噪声模型,经反复迭代调整像素点的噪声值直至最优.在FCM反复迭代的过程中,对算法进行上下截集半模糊化处理,从而提高分类的速率和准确率....     

Unbiased homomorphic system and its application in reducing multiplicative noise

The problem of reducing the multiplicative noise corrupting a signal is discussed. A generalisation of the existing sampled function weighted order (SFWO) filter is proposed by relaxing the symmetry condition on the probability density function (PDF) of the noise. This generalised SFWO filter is then used within a homomorphic system to reduce the multiplicative noise. It is shown that the output from such a system is biased, and hence, a suitable bias compensation technique is suggested. An unbiased homomorphic system, whose design is based on the PDF of the corrupting multiplicative noise, is proposed to reduce the noise. Images generated by coherent imaging systems are always corrupted by speckle, a kind of multiplicative noise having a lognormal distribution. A filter called the mean median filter, to reduce additive white Gaussian noise, is first proposed and then used within the unbiased homomorphic system to reduce the speckle in images. The effectiveness of the various proposed algorithms is demonstrated and compared with that of some of the existing schemes through extensive simulations     

Medical image noise reduction using the Sylvester-Lyapunov equation

Multiplicative noise is often present in medical and biological imaging, such as magnetic resonance imaging (MRI), Ultrasound, positron emission tomography (PET), single photon emission computed tomography (SPECT), and fluorescence microscopy. Noise reduction in medical images is a difficult task in which linear filtering algorithms usually fail. Bayesian algorithms have been used with success but they are time consuming and computationally demanding. In addition, the increasing importance of the 3-D and 4-D medical image analysis in medical diagnosis procedures increases the amount of data that must be efficiently processed. This paper presents a Bayesian denoising algorithm which copes with additive white Gaussian and multiplicative noise described by Poisson and Rayleigh distributions. The algorithm is based on the maximum a posteriori (MAP) criterion, and edge preserving priors which avoid the distortion of relevant anatomical details. The main contribution of the paper is the unification of a set of Bayesian denoising algorithms for additive and multiplicative noise using a well-known mathematical framework, the Sylvester-Lyapunov equation, developed in the context of the Control theory.