自适应调节滤波强度的SAR图像非局部平均抑斑算法

为提升对SAR图像乘性相干斑的抑制水平与边缘保护性能,该文提出了一种可自适应调节滤波强度(AFS)的SAR图像非局部平均(NLM)抑斑新算法(AFS-NLM).该算法利用Frost滤波图像计算的局部均值与方差来改善SAR图像场景参量的估计,形成了一种能更好刻画SAR图像同质区与边缘区的改进Kuan滤波系数.利用局部均值比与改进Kuan滤波系数分别作为新的相似性测量参量与自适应衰减因子,构建了一种更适应SAR图像乘性噪声特性的改进NLM滤波.利用偏平滑参数与偏边缘保护参数控制下的改进NLM滤波,分别替代经典Kuan滤波模型中的像素局部均值与自身灰度值作为加权项,并采用由改进Kuan滤波系数构建的自适应调节因子对二者进行加权平均,从而形成了一种可自适应调节滤波强度的加权滤波新模型.实验表明,该文算法与近期多种先进算法相比,具有更好的相干斑抑制与边缘保护性能.     

抑制SAR图像相干斑的迭代方向滤波算法

为保护SAR图像边缘特征并有效提高对乘性相干斑噪声的抑制性能,该文提出一种基于迭代方向滤波的抑制图像相干斑新算法。该算法先借助高斯-伽马平行窗估计出的比率边缘强度映射(ESM)与方向信息,自适应地控制各向异性高斯核(AGK),生成沿ESM方向分布的具有各向异性支撑区域的局域窗。然后将SAR图像多种局部统计参量联合作为衰减因子,形成与SAR图像区域分布特性相适应的负指数衰减型加权系数,进而将负指数衰减型加权系数与局域窗带方向的各向异性支撑区域结合形成局域加权的方向滤波。最后对SAR图像迭代地进行方向滤波即可实现带边缘保护的相干斑抑制。实验结果表明,与多种抑斑算法相比,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。     

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。     

基于变差系数的SAR图像非局部均值滤波算法

SAR(synthetic aperture radar)图像有固有的乘性相干斑噪声,抑制相干斑噪声和保护边缘信息是研究的重要内容。文章提出了一种变差系数差构建的非局部平均滤波算法DCV-NLM(differenceof coefficient of variation non-local means)。首先,搜索窗口中的变差系数的像素与中心像素在相似窗口中之间的局部差,其次,局部差由两个范数构造的相似性参数,用负指数形式得到每个像素的加权系数,最后是对SAR图像的噪声执行加权滤波。通过提出的算法与IDPAD算法对比,从抑制相干斑图像视觉效果方面对比,DCV-NLM算法比起IDPAD算法有更好的相干斑抑制性能。     

基于非局部均值滤波的SAR图像去噪

该文提出一种基于结构相似性指数(SSIM)的非局部均值(Non Local means,NL-means)滤波的合成孔径雷达(SAR)图像相干斑噪声抑制新方法。该方法用SSIM改进NL-means算法中小块相似性的度量,能利用结构信息来进行相干斑抑制。通过在真实SAR图像上的实验表明,与GammaMAP滤波、CHMT算法、BLS-GSM算法、NL-means滤波相比,此方法在有效去除相干斑噪声的同时能更好地保持边缘结构信息。     

一种小波域改进非局部均值滤波算法

提出了一种噪声图像高效滤波算法。该算法对经典非局部均值滤波算法从边缘保持效果和计算复杂度两个方面加以改进。提出一种基于图像结构相似度(SSIM)相似性检测算子,并将其与传统的高斯加权欧氏距离进行加权融合,从而实现对经典非局部均值滤波的改进,可实现对图像边缘和平坦区域滤波的有效兼顾。将其引入到小波变换域,对于高频子图像,首先采用Canny算子实现自适应边缘检测,获得边缘和非边缘图像,采用改进非局部均值滤波和经典非局部均值滤波分别加以处理,然后实现图像的融合;最后实现小波系数重构。通过对实物图像和标准测试图像的仿真实验结果表明,该滤波算法的去噪效果较优,能基本实现对高强度随机噪声情形下的图像复原,从而印证了该滤波思路的可行性。     

基于局部混合滤波的SAR图像边缘检测

该文结合基于非下采样方向滤波-双树复小波变换(NonSubsampled Direction Filter Bank-Dual-Tree Complex Wavelet Transform, NSDFB-DTCWT)的局部混合滤波算法和Dempster-Shafet (DS)证据理论提出一种基于局部混合滤波的 SAR 图像边缘检测算法.该算法首先对 SAR 图像进行局部混合滤波,然后对不同尺度滤波图像使用指数加权均值比(Ratio Of Exponentially Weighted Averages, ROEWA)算子检测边缘的强度,再使用Canny算子检测边缘的方向,从而得到SAR图像各尺度上的边缘,最后使用DS证据理论融合各尺度的边缘形成原始 SAR 图像的边缘.实验结果表明:该文所提出的算法具有很好的边缘检测效果,检测到的 SAR 图像的边缘定位准确和完整,且伪边缘较少.     

基于自适应Bilateral滤波的SAR图像相干斑抑制

SAR图像的相干斑抑制一直是SAR图像预处理的关键步骤。Bilateral滤波器是一类可以同时利用像素间的空间相似性与灰度相似性进行图像去噪的算法。针对直接用Bilateral滤波器进行相干斑抑制时存在空间方差系数选择困难及灰度相似性模型不合理的问题,该文提出一种自适应Bilateral滤波算法,该算法根据图像的局域变差系数自动调整空间方差系数,利用SAR图像的似然概率函数代替高斯函数来进行灰度相似性度量。理论分析及实验表明,提出方法较相关算法有更强的噪声平滑能力和更好的细节保持能力。     

结构保持的双边滤波极化SAR图像降噪

针对极化合成孔径雷达(Polarimetric Synthetic Aperture Radar, PolSAR)图像相干斑抑制时结构保持的难题,该文提出一种PolSAR图像的双边滤波算法:结构保持的双边滤波(SPBF)。该算法通过结合边缘结构特征和地物散射特性,增强对PolSAR图像结构信息的描述,减少滤波时图像结构信息的损失,实现滤波性能的提高。该算法首先使用边缘检测模板在极化总功率图像(Span)上提取边缘方向,实现自适应选择滤波方向窗;其次,采用Freeman-Durden分解获取像素的散射机制,并根据极化数据的统计分布特性获取地物散射的聚类标记;最终在所选的方向窗中,以聚类标记图为掩膜,利用改进的双边滤波算法对PolSAR数据进行相干斑抑制。真实SAR数据的实验结果表明,该方法能够有效抑制相干斑噪声,同时提高了对图像的边缘、强点目标和极化散射特性的保持能力。     

一种考虑边缘检测的自适应窗口相干斑抑制

本文针对传统SAR图像相干斑空域滤波使用固定窗口所带来的问题,提出一种采用边缘检测和自适应滑动窗口技术联合降噪的相干斑抑制算法。该算法先把初始窗口的所有像素作为样本并统计样本局域方差系数,然后与初始阈值作比较:如果小于初始阈值,则采用方形窗口自适应滑动窗口算法进行滤波,否则采用利用边缘检测确定的变形窗口自适应滑动窗口算法进行滤波。仿真图像和真实雷达图像降噪实验结果表明,该算法在边缘保持和去噪方面具有更好的滤波性能。     

SAR image despeckling via bilateral filtering

Bilateral filtering (BF) can realise both smoothing images and preserving edges, whereas its filtering results are always influenced since its two parameters are difficult to configure to the optimum. In this reported work, the application of BF is extended to synthetic aperture radar (SAR) image despeckling, and the despeckling evaluation indexes, including the equivalent number of looks and the edge save index, are used to estimate the parameters. After BF with estimated parameters imposed on a normalised SAR image, further processing can achieve both despeckling and edge preservation simultaneously. Experimental results show that the visual quality and evaluation indexes of the proposed algorithm outperform the classical Lee filtering.     

基于斑点方差估计的非下采样Contourlet域SAR图像去噪

 合成孔径雷达(SAR)图像固有的相干斑噪声严重影响图像质量,使得SAR图像的自动解译十分困难.本文联合SAR图像的统计特性和非下采样Contourlet对SAR图像细节信息的良好刻画能力,提出一种新的非下采样Contourlet域SAR图像去噪算法,通过估计到的各个高频方向子带的斑点噪声方差和变换系数模值的局部均值,对非下采样Contourlet变换系数进行判定,保留信号系数,抑制斑点噪声系数,实现SAR图像去噪.仿真实验结果表明,本文方法在斑点抑制的同时可以有效保持细节信息.     

采用GSM模型进行稀疏表示的SAR图像降斑算法

针对SAR图像降斑过程中会产生过平滑现象及相干斑的滤除不彻底等问题,提出了稀疏结构符合高斯比例混合（Gaussian Scale Mixture,GSM）模型的SAR图像降斑算法。根据贝叶斯原理以及相干斑的统计特性推导该算法的数学模型,在块匹配过程中使用概率而不是欧式距离进行权重衡量,根据图像块之间的结构相似度,可以有效区分同质区与异质区,并得到图像块的较优均值估计。使用PCA字典学习方法对每个图像块进行子字典训练,实现同步稀疏编码（Simultaneous Sparse Coding,SSC）,数学模型的求解利用迭代正则化方法。分别使用合成场景SAR图像及真实场景SAR图像对算法进行验证,实验表明,相比于目前已提出的PPB算法、SAR-BM3D算法及FANS算法,该算法能有效提高等效视数,在滤除相干斑的同时很好地保留了图像的局部结构特性与纹理特征。      

冗余轮廓波变换的构造及其在SAR图像降斑中的应用

构造了由非抽样塔式分解和方向滤波器组实现的冗余轮廓波变换。文中利用McClellan变换设计非抽样塔式分解中满足精确重构条件的圆对称滤波器组。利用冗余轮廓波变换系数的自适应局部统计模型及最大后验概率法对SAR图像进行降斑处理,并与基于平稳小波和轮廓波变换的降斑算法进行比较。结果表明,提出的算法能有效地去除散斑噪声,并且具有更强的边缘保持能力。     

Speckle filtering of SAR images based on adaptive windowing

Speckle noise usually occurs in synthetic aperture radar (SAR) images owing to coherent processing of SAR data. The most well-known image domain speckle filters are the adaptive filters using local statistics such as the mean and standard deviation. The local statistics filters adapt the filter coefficients based on data within a fixed running window. In these schemes, depending on the window size, there exists trade-off between the extent of speckle noise suppression and the capability of preserving fine details. The authors propose a new adaptive windowing algorithm for speckle noise suppression which solves the problem of window size associated with the local statistics adaptive filters. In the algorithm, the window size is automatically adjusted depending on regional characteristics to suppress speckle noise as much as possible while preserving fine details. Speckle noise suppression gets stronger in homogeneous regions as the window size increases succeedingly. In fine detail regions, by reducing the window size successively, edges and textures are preserved. The fixed-window filtering schemes and the proposed one are applied to both a simulated SAR image and an ERS-1 SAR image to demonstrate the excellent performance of the proposed adaptive windowing algorithm for speckle noise     

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     

基于低秩分解和改进的非局部平均的SAR图像相干斑抑制

为抑制合成孔径雷达（SAR）图像成像过程中形成的相干斑噪声,提出了一种基于低秩分解和改进的非局部平均的SAR图像相干斑去噪方法。首先将SAR图像进行对数处理,将乘性噪声转换为加性噪声;然后利用低秩稀疏分解将对数图像分解成低秩图像部分和稀疏图像部分;接着对含噪严重的稀疏图像部分分析其结构张量,生成非局部平均滤波所需的衰减因子,进行改进的非局部平均滤波去噪;最后再做图像合成,经指数变换得到去噪后的SAR图像。实验结果表明,该方法经视觉评价、边缘保持指数（EPI）和等效视数（ENL）等方面评测,具有较好的抑制噪声和保持边缘及纹理细节的能力。      

Despeckling low SNR, low contrast ultrasound images via anisotropic level set diffusion

Speckle is a form of multiplicative and locally correlated noise which degrades the signal-to-noise ratio (SNR) and contrast resolution of ultrasound images. This paper presents a new anisotropic level set method for despeckling low SNR, low contrast ultrasound images. The coefficient of variation, a speckle-robust edge detector is embedded in the well known geodesic “snakes” model to smooth the image level sets, while preserving and sharpening edges of a speckled image. The method achieves much better speckle suppression and edge preservation compared to the traditional anisotropic diffusion based despeckling filters. In addition, the performance of the filter is less sensitive to the speckle scale of the image and edge contrast parameter, which makes it more suitable for the detection of low contrast features in an ultrasound image. We validate the method using both synthetic and real ultrasound images and quantify the performance improvement over other state-of-the-art algorithms in terms of speckle noise reduction and edge preservation indices.