SAR image filtering based on the heavy-tailed Rayleigh model.

Synthetic aperture radar (SAR) images are inherently affected by a signal dependent noise known as speckle, which is due to the radar wave coherence. In this paper, we propose a novel adaptive despeckling filter and derive a maximum a posteriori (MAP) estimator for the radar cross section (RCS). We first employ a logarithmic transformation to change the multiplicative speckle into additive noise. We model the RCS using the recently introduced heavy-tailed Rayleigh density function, which was derived based on the assumption that the real and imaginary parts of the received complex signal are best described using the alpha-stable family of distribution. We estimate model parameters from noisy observations by means of second-kind statistics theory, which relies on the Mellin transform. Finally, we compare the proposed algorithm with several classical speckle filters applied on actual SAR images. Experimental results show that the homomorphic MAP filter based on the heavy-tailed Rayleigh prior for the RCS is among the best for speckle removal.     

基于同态滤波与自适应模糊多级中值滤波级联算法的散斑噪声污染图像恢复

提出了一种用于恢复散斑噪声污染图像的同态滤波与自适应模糊多级中值滤波级联算法 ,计算机仿真实验结果表明它既保持了图像的几何结构 ,又有效地抑制了散斑噪声 ,并且通过比较证明它优于同态滤波与多级中值滤波级联算法     

激光主动成像图像噪声抑制方法

针对激光主动成像图像特点及实际应用需要,提出了一种基于同态滤波与双数复值小波变换级联的图像降噪算法。首先通过同态滤波将乘性散斑噪声变换为加性噪声;然后用基于改进Q-shift滤波器的双树复值小波对含噪图像进行分解,通过Bayes自适应阈值法修正小波系数;最后再进行相应的逆变换得到去噪图像。该算法具有近似平移不变性、多方向选择性及精确重构性,采用信噪比（SNR）、峰值信噪比（PSNR）和运行时间作为算法去噪性能的评价标准进行实验。实验结果表明该算法能够有效抑制图像中的散斑噪声,计算效率高,且很好地保护了图像细节。     

自适应滤波在激光雷达中的应用

针对存在斑纹和湍流这两类乘性噪声的激光雷达系统,讨论了对数回波功率的估计问题。在模型参数未知的情况下,在自适应滤波中采用分割法,并给出了自适应滤波方程。仿真结果表明,在同时存在斑纹和湍流噪声的情况下,本文给出的自适应滤波算法优于只考虑斑纹噪声的滤波算法。     

激光成像雷达系统中散斑像的乘法模型及其滤除

分析了激光成像雷达系统中所成的散斑像的乘法模型 ,并在此基础上利用一基于小波的同态滤波器对被散斑污染的图像进行了消噪处理。计算机仿真结果表明此方法能有效地抑制散斑噪声并较好地保留图像的边缘信息 ,证明了乘法模型能对散斑像起到较好的近似作用     

Biased motion-adaptive temporal filtering for speckle reduction in echocardiography

Describes a new fully motion-adaptive spatio-temporal filtering technique to reduce the speckle in ultrasound images. The advantages of this approach are demonstrated in echocardiographic boundary detection and in comparison with other techniques. The first stage of many automated echocardiographic image interpretation schemes is filtering to reduce the amount of speckle noise. The authors show how the two-dimensional least mean squares (TDLMS) filter can be configured as a motion-compensated filter for a time sequence of ultrasound images that eliminates the blurring associated with direct averaging. For an image corrupted by multiplicative speckle noise, the mode of the intensity distribution approximates the maximum likelihood estimator. In consequence, the temporal filter's output is biased towards the mode from the mean, using information contained within the speckle itself. A new adaptive algorithm for controlling the filter's convergence is also included. To evaluate performance, application to simulated, phantom, and an in vivo test sequence of the carotid artery are considered in comparison with other techniques. The effect of filtering on edges is of great importance, as these are used by subsequent image interpretation schemes. Quantitative measurements demonstrate the effectiveness of the Biased TDLMS filter, for both noise reduction and edge preservation. Echocardiographic images have a high noise content and suffer from poor contrast. Despite this challenging environment, the Biased TDLMS filter is shown to produce images that are better inputs for subsequent feature extraction. The benefits for echocardiographic images are highlighted by considering the problems of mitral valve analysis and extraction of the left atrium boundary.     

Two-dimensional adaptive block Kalman filtering of SAR imagery

A method for removing speckle from synthetic aperture radar (SAR) imagery by using 2-D adaptive block Kalman filtering is introduced. The image process is represented by an autoregressive model with a nonsymmetric half-plane (NSHP) region of support. New 2-D Kalman filtering equations are derived which taken into account not only the effect of speckles as multiplicative noise but also the effects of the additive receiver thermal noise and the blur. This method assumes local stationarity within a processing window, whereas the image can be assumed to be globally nonstationary. A recursive identification process using the stochastic Newton approach is also proposed which can be used on-line to estimate the filter parameters based upon the information within each new block of the image. Simulation results on several images are provided to indicate the effectiveness of the proposed method when used to remove the effects of speckle noise as well as those of the additive noise     

基于同态滤波和全变差的激光图像降噪方法

针对激光主动成像图像的特点,提出了一种基于同态滤波和全变差的图像降噪方法.首先对图像进行同态滤波,提高图像的对比度并去除激光图像的散斑噪声,然后采用基于最小化全变差模型去除激光图像的高斯噪声和脉冲噪声.采用信噪比、对比度和亮度失真度作为图像降噪效果的评估,将算法与中值降噪、小波与中值滤波结合降噪等进行对比实验,实验结果...     

基于合成孔径雷达的SAR图像去噪

针对SAR图像相干斑抑制问题.提出一种双变量收缩函数与小波系数显著性增强相结合的SAR图像的斑点抑制算法.文中将双树复小波推广至斑点噪声模型,利用相邻尺度小波系数的联合概率密度函数与噪声的统计模型联立后.通过最大后验概率估计出滤波后图像的小波系数.再采用小波系数的模极大值准则对系数进行显著性增强.突出图像的边缘特征和点特征.仿真实验表明.与其他传统的去噪算法相比.本文提出的算法具有更好的去噪效果.     

基于一种新的同态滤波算法的散斑噪声压缩

提出一种新的用于压缩散斑噪声的同态滤波算法。它在同态变换后寻求滤波窗中的最均匀区域,以其灰度均值替代滤波窗中心像素值,重复迭代至其值基本不变,最后作同态变换的逆变换。仿真结果表明它较好的保持了图像的边缘和较有效地抑制了散斑噪声     

A review of speckle filtering in the context of estimation theory

Speckle filter performance depends strongly on the speckle and scene models used as the basis for filter development. These models implicitly incorporate certain assumptions about speckle, scene, and observed signals. In this study, the multiplicative and the product speckle models, which have been used for the development of most of the well-known filters, are analyzed, and their implicit assumptions with regard to the stationarity-nonstationarity nature of speckle are discussed. This leads to the definition of two categories of speckle filters: the stationary and the nonstationary multiplicative speckle model filters. The various approximate models used for the multiplicative speckle noise model are assessed as functions of speckle and scene characteristics to derive the requirements on scene signal variations for the validity of both the stationary and nonstationary multiplicative speckle models. Speckle filtering is then studied in the context of estimation theory, so as to develop a procedure for speckle filtering. It is shown that speckle filtering can be effective only in locally stationary scenes. Regions in which the signals are not stationary have to be filtered separately using a priori scene templates for the best matching of nonstationary scene features. The use of multiresolution techniques is crucial for accurate estimation of filter parameters. Under the guidance of the speckle filtering procedure, structural-multiresolution versions of the Lee (1980) and Frost et al. (1982) filters are developed for optimum application of these filters in the context of nonstationary scene signals.     

反射式离轴数字全息再现像中的噪声抑制

为了抑制反射式离轴数字全息再现像中的零级像和散斑噪声对再现像的影响,利用高斯核函数在空域对数字全息图进行低通滤波,再用原数字全息图减去滤波后的低频信息来抑制零级像,采用非局部均值滤波来抑制散斑噪声。实验结果表明空域高斯核函数处理法能有效地抑制零级像,非局部均值滤波可以抑制散斑噪声但对部分细节的保持能力不强,改进后的非局部均值滤波在抑制散斑噪声的同时能很好地保护细节信息,提高了离轴数字全息再现像的质量。     

基于同态滤波的电子散斑干涉图像处理

由于散斑具有可测的强度和确定的相位,正逐渐被人们重视和进一步研究,现广泛应用于无损检测领域。电子散斑干涉条纹图上总是附带大量的随机散斑颗粒噪声。一般来说,散斑图像记录的变形信息主要集中在低频部分,而噪声主要在高频部分。为了得到变形物体的连续相位分布,需要用适当的方法对散斑干涉条纹图像进行滤波降噪处理。同态滤波是一种将亮度范围压缩和对比度增强的频域处理方法。采用基于同态滤波原理设计出的新的低通滤波器,将其运用于3步相移电子散斑干涉条纹图像处理,并从理论上进行了详细的论述。结果表明,运用该滤波器处理散斑条纹图像,过滤了大量的散斑颗粒噪声,增强了散斑干涉条纹的对比度。     

Analytic behavior of the LMS adaptive line enhancer for sinusoidscorrupted by multiplicative and additive noise

The least mean squares adaptive line enhancer (LMS ALE) has been widely used for the enhancement of coherent sinusoids in additive wideband noise. This paper studies the behavior of the LMS ALE when applied to the enhancement of sinusoids that have been corrupted by both colored multiplicative and white additive noise. The multiplicative noise decorrelates the sinusoid, spreads its power spectrum, and acts as an additional corrupting noise. Closed-form expressions are derived for the optimum (Wiener filter) ALE output SNR as a function of the residual coherent sine wave power, the noncoherent sine wave power spectrum, and the background additive white noise. When the coherent to noncoherent sine wave power ratio is sufficiently small, it is shown that a nonlinear (e.g., square law) transformation of the ALE input results in a larger optimum ALE output SNR     

Toward edge sharpening: a SAR speckle filtering algorithm

This paper makes two contributions. It first introduces an algorithm for synthetic aperture radar (SAR) speckle reduction and edge sharpening. Existing speckle filtering algorithms can effectively reduce the speckle effect but unfortunately also, to some degree, smear edges and blur images. Even for unfiltered images, there is still a need for edge sharpening; since SAR sensors have limited bandwidths, leading to slow responses to sudden changes (smearing sharp edges). The proposed algorithm functions as an adaptive-mean filter. Edge crossing points are detected by using the second-order derivative of the Gaussian function as a wavelet transform function. A proper dilation scale factor enables the wavelet transform function to detect only edge crossings and ignore the local oscillations. Then in a moving window the mean filter is applied if there is no edge crossing point. Otherwise, averaging is only applied to the part of the window separated by edge crossing points. Consequently, the algorithm smooths uniform areas while it sharpens and enhances edges. Edges of the filtered images are generally sharper than the original. Similarities between the proposed filter and other popular speckle filters, such as the Lee, Kuan, and Frost filters, designated for SAR multiplicative noise, are analyzed. Another contribution of the paper is the evaluation of popular filters from the viewpoint of texture preservation. The evaluation is carried out using the first and second-order histograms. Possible distortions caused by filters are explained     

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MATLAB的数字散斑条纹图滤波比较

数字散斑照相术提取的数字散斑条纹图存在信噪比低、强度弱的特点,因此需要对图像进行滤波除噪。为了比较不同的滤波方法对散斑条纹图的除噪效果,笔者在MATLAB中采用中值滤波、巴特沃斯低通滤波、指数低通滤波以及同态滤波对提取的条纹图进行处理并就处理结果做出了分析比较。实验结果表明,以上方法都能从噪声中提取信号,其中中值滤波除噪效果最好。     

A minimum misadjustment adaptive FIR filter

The performance of an adaptive filter is limited by the misadjustment resulting from the variance of adapting parameters. This paper develops a method to reduce the misadjustment when the additive noise in the desired signal is correlated. Given a static linear model, the linear estimator that can achieve the minimum parameter variance estimate is known as the best linear unbiased estimator (BLUE). Starting from classical estimation theory and a Gaussian autoregressive (AR) noise model, a maximum likelihood (ML) estimator that jointly estimates the filter parameters and the noise statistics is established. The estimator is shown to approach the best linear unbiased estimator asymptotically. The proposed adaptive filtering method follows by modifying the commonly used mean-square error (MSE) criterion in accordance with the ML cost function. The new configuration consists of two adaptive components: a modeling filter and a noise whitening filter. Convergence study reveals that there is only one minimum in the error surface, and global convergence is guaranteed. Analysis of the adaptive system when optimized by LMS or RLS is made, together with the tracking capability investigation. The proposed adaptive method performs significantly better than a usual adaptive filter with correlated additive noise and tracks a time-varying system more effectively     

基于混合迭代滤波的SAR图像相干斑抑制

该文将变窗Lee滤波与自蛇扩散结合,提出了一种抑制SAR图像相干斑噪声的混合迭代滤波新算法,并给出了SAR图像噪声方差的一种有效估计方法。该算法首先估计SAR图像局部统计参量,进而通过改进的变窗Lee滤波对SAR图像进行平滑去噪,接着利用自蛇扩散去除Lee滤波难以平滑的孤立点噪声与边缘噪声。在Lee滤波与自蛇扩散的混合迭代中,Lee滤波的窗尺度随迭代次数的增加而增大,从而在保护SAR图像边缘细节的同时,同质区得到更好平滑。实验结果表明,与已有多种抑斑算法相比,该文算法在SAR图像抑斑与边缘保护方面均获得了更好的性能。     

Adaptive filter applications to LIDAR: return power and log powerestimation

The problem of estimating the return power in a LIDAR system in the presence of multiplicative noise (speckle) is addressed. A significant class of the partitioning approach is applied and comparisons are made with the extended Kalman filter (EKF) in the case where model parameter uncertainty exists. Through extensive simulations, the partitioned filter is shown to be significantly superior to the EKF algorithm