基于Cxlmage库的SAR图像Speckle噪声抑制

合成孔径雷达图像的相干斑噪声抑制是SAR信息处理中的一个重要环节.经典的Speckle噪声抑制通常作为一个处理模块集成在软件中,但现有软件代码封装无法二次开发.针对文中提出了一种基于开源图像库CxImage的空间域自适应Speckle噪声抑制算法的应用与集成.将CxImage图像库链接入MFC应用程序框架中,利用其图像管理、维护、处理功能对SAR图像进行维护管理,集成多种经典的空间域自适应滤波方法,并以ERS-2卫星的PRISAR数据为例,进行Speckle噪声滤波处理,选取适当的滤波效果评价参数,对滤波结果进行比较,最终得出各滤波算法应用于SAR图像滤波的优劣.     

基于双正交小波域的SAR图像滤波方法

将E-lee、E-Kuan、GammaMap、wiener等经典滤波算法和双正交小波变换相结合,提出了基于双正交小波变换域的局部统计特性SAR图像滤波方法,同时提出了一个运算量少,且是归一化的对数变换,它将乘性的Speckle噪声转为加性噪声。在小波域内建立了局部统计特性SAR图像滤波算法,使用多分辨率的手段,因为在每个方向上的小波系数都具有相同的特征,可以很好地处理图像的一些特性,使得图像边缘被模糊的相对少些。实验结果表明,此方法比经典算法的效果要好。     

基于自适应小波阈值的SAR图像降噪

合成孔径雷达(SAR)图像固有的相干斑噪声严重降低了图像质量,影响了后续的处理.SAR图像降噪的要求,是在滤除噪声的同时, 尽量保持原始图像的边缘和纹理等细节信息.本文将经典的小波域空间自适应软阈值法应用于降低SAR图像斑点噪声.在此基础上,本文提出根据小波系数的局部统计量选取阈值以及估计相关参数的方法.实验结果表明,本文算法在降低噪声和保持边缘纹理细节之间取得了较好的平衡,处理后的SAR图像的视觉效果和质量评估参数都要优于传统的空间域滤波方法的处理结果.     

一种基于Shearlet变换域改进自适应中值滤波算法

为了有效滤除图像中大量存在的脉冲噪声,提出了一种基于Shearlet变换域改进自适应中值滤波方法。首先在对Shearlet变换进行深入分析的基础上,给出了Shearlet分解和重构基本步骤;然后实现对含噪图像进行多尺度Shearlet变换,对获得多个尺度下的分解系数采用从噪声检测、噪声滤波等环节改进的自适应中值滤波算法(IAMF)进行噪声抑制;最后实现滤波后分解系数重构。分别与经典中值滤波(MF)、自适应中值滤波(AMF)以及Shearlet变换域阈值法进行比较,实验结果表明,该滤波算法滤波性能较好。     

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。     

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.     

基于AOS非线性扩散的SAR图像去噪研究

针对合成孔径雷达(SAR)图像的斑点噪声,介绍了一种基于非线性各向异性扩散的去噪方法,该方法经过加性算子分离(AOS)方案离散可以保证其扩散迭代过程中滤波结果的绝对稳定,并且利用其在消除噪声的同时锐化边界的特点,将之引入SAR图像的斑点噪声抑制问题当中.通过对一幅SAR图像的滤波处理,以及若干衡量滤波算法效果的评价指标,将其与传统的自适应局域统计滤波方法进行分析比较,并得出相关结论,从而证实了该研究提出的AOS非线性扩散滤波法(AOS ND法)抑制SAR图像相干斑的可行性和有效性.     

一种新的SAR图像相干斑抑制算法

针对经典局域统计自适应滤波器的窗口大小问题,提出了一种新的用于抑制合成孔径雷达图像相干斑噪声的滤波算法.基于SAR图像的统计特性,窗口大小根据区域特征自动调整,尽可能将窗口调整为包含同一均匀区域,从而在保持细节的同时尽最大可能地抑制斑点噪声.实验结果表明,本算法不仅有效抑制了SAR图像的相干斑,保持图像的边缘细节.而且有着良好的图像视觉效果.     

基于图像增强的改进自适应加权均值滤波算法

为了尽可能滤除图像中的椒盐噪声同时改善图像视觉效果,将改进自适应加权均值滤波与小波域图像增强技术有机结合,提出了一种具有增强效果的图像滤波算法。该算法分为滤波和滤波后处理两个阶段。滤波阶段,对经典均值滤波分别从噪声检测策略、权值计算机方法噪声滤波模版设计等方面进行适当改进,给出了具体实现步骤;滤波后处理阶段,首先将滤波后图像进行三层小波分解;然后构造出一种小波图像增强模型,根据小波系数的幅度值将其分为三个部分,分别进行不同程度的拉伸处理;最后进行拉伸后小波系数重构。将该滤波算法与经典均值滤波,加权均值滤波、自适应加权中值滤波等性能比较,实验结果表明,本文滤波算法在噪声滤除和图像细节保持方面,效果较好。     

一种基于非线性域值小波包的超声图像闪斑抑制方法

在B超三维成像中,采用了一种新的基于非线性域值小波包的闪斑（Speckle)噪声抑制方法来提高成像质量。首先将一幅原始图像经过预滤波分解为高频噪声图像和低频主体图像两部分,然后分别经过多分辨率小波包处理,得到一组小波包域的系统。该系统采用一种软阈值滤波算法处理,去除掉其中的噪声并保留下各自的有用信息。两组分别去噪后的小波包系数经反变换回到时域并相加,得到消除了噪声的图像。该算法的性能在计算机仿真图像上进行了验证。采用该方法处理了一组水槽中的碗状物体的B超图像,并进行了三维体视化重构。实验表明,这种方法可以有效地抑制超声闪斑噪声并在重构中保留对象的细节特征。     

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     

基于统计理论的SAR图像斑点噪声滤波算法分析

合成孔径雷达图像固有的斑点噪声严重降低了图像的可解译程度,影响了后续目标检测、分类和识别等应用。文中通过对基于统计理论的SAR图像斑点噪声滤波方法进行比较分析,得出单个滤波器难以从去噪和边缘保持方面均达到最佳效果的结论。     

Restoration of speckle noise corrupted SAR images using regularization by denoising

Speckle noise removal is a well-established problem in synthetic aperture radar (SAR) image processing. Among different methods focused on the reconstruction of SAR images, variational models have achieved state-of-the-art performance. In this paper, a Rayleigh based speckle reduction algorithm is developed using the variational framework. The forward model is combined with recently proposed regularization by denoising (RED) prior. However, RED has been proposed in literature for the additive noise model. Multiplicative noise in SAR images prevents the direct application of RED to variational models. Hence, logarithm transformation is applied to change the multiplicative noise model to additive model, and the forward model from Rayleigh to Fisher–Tippett distribution. The resulting optimization problem is solved using the alternating direction method of multipliers. Further, the proof of the convergence analysis is carried out for the above framework. Simulations convey that the proposed method has better despeckling performance compared to that of state-of-the-art methods.     

小波包域值法去除合成孔径雷达图像斑点噪声

合成孔径雷达(SAR)图像的斑点噪声使图像的解译能力明显降低,影响了SAR图像的应用,去除图像斑点噪声是SAR图像处理及应用的前提。去除的方法在图像域有很多,在频率域,也有基于FFT变换和小波变换的许多方法。文中根据沙漠地区的SAR图像的特点,对基于图像域和频率域的几种滤波方法进行了比较,提出了一种基于小波包变换的SAR图像斑点噪声去除方法,结果表明该方法既去除了斑点噪声,又保持了边缘特征信息。     

SAR Image Regularization With Fast Approximate Discrete Minimization

Synthetic aperture radar (SAR) images, like other coherent imaging modalities, suffer from speckle noise. The presence of this noise makes the automatic interpretation of images a challenging task and noise reduction is often a prerequisite for successful use of classical image processing algorithms. Numerous approaches have been proposed to filter speckle noise. Markov random field (MRF) modelization provides a convenient way to express both data fidelity constraints and desirable properties of the filtered image. In this context, total variation minimization has been extensively used to constrain the oscillations in the regularized image while preserving its edges. Speckle noise follows heavy-tailed distributions, and the MRF formulation leads to a minimization problem involving nonconvex log-likelihood terms. Such a minimization can be performed efficiently by computing minimum cuts on weighted graphs. Due to memory constraints, exact minimization, although theoretically possible, is not achievable on large images required by remote sensing applications. The computational burden of the state-of-the-art algorithm for approximate minimization (namely the alpha -expansion) is too heavy specially when considering joint regularization of several images. We show that a satisfying solution can be reached, in few iterations, by performing a graph-cut-based combinatorial exploration of large trial moves. This algorithm is applied to joint regularization of the amplitude and interferometric phase in urban area SAR images.     

SAR image despeckling using heavy-tailed Burr distribution

Multiplicative speckle noise diminishes the radiometric resolution of the synthetic aperture radar (SAR) images and all the coherent images. Speckle removal adds an extra value to an automated SAR image interpretation and analysis. In this paper, dual-tree complex wavelet-transform-based Bayesian method is proposed for despeckling the SAR images. In each subband, the reflectance and noise of the logarithmically transformed wavelet coefficients are modeled using heavy-tailed Burr and zero-mean Gaussian distributions. The closed-form expression for the shape parameter of Burr distribution is derived by employing the Mellin transform. The resultant complex-free quadratic maximum a posteriori solution with suitable shrinkage function yields despeckled SAR images. Extensive experiments are carried out using real SAR images as well as simulated images. The proposed method performs well in terms of equivalent number of looks with 3.5751 dB improvement in homogeneous region1 of Pipe river SAR image, edge preservation with 0.6158 improvement, peak signal to noise ratio of 51.3305 dB, and mean structural similarity index measure of 0.9397 at 0.05 noise variance for synthetically speckled image in comparison to the existing methods and takes averagely 2.3461 times less computing time. The proposed method provides a computationally efficient better speckle reduction in homogeneous regions while still preserving the edge.     

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。      

基于SLIC超像素分割的SAR图像海陆分割算法

海陆分割在合成孔径雷达(SAR)图像的海面目标检测以及海岸线提取等海洋应用方面具有非常重要的意义。针对合成孔径雷达图像的特点,提出了基于SLIC超像素分割的SAR图像海陆分割算法。首先为抑制SAR图像固有相干斑噪声并较好地保留图像的边缘信息,采用精致Lee滤波对图像进行预处理。然后对图像进行SLIC超像素分割,再将分割后的图像进行FT区域显著性检测以及显著值相似度聚类。最后将处理后的图片二值化得到海陆分割结果。实验结果表明,本文所提海陆分割算法具有很高的处理精度以及较高的处理效率。     

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.