改进的基于结构检测的SAR图像去斑方法

合成孔径雷达图像固有的相干斑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.因此,SAR图像的相干斑抑制问题一直是SAR图像应用的重要课题之一.一个理想的去斑算法应该在平滑的同时保持图像的边缘等细节不受损失,目前存在各种各样的算法,但没有一种方法能够完美的满足这一要求.为此该文提出了一种改进的结构检测的SAR图像去斑算法.利用概率迭代方法分割图像并检测边缘,结合强点检测图,将SAR图像标为结构区和非结构区,在非结构区域内进行Lee滤波以平滑噪声,对结构区直接保留原值,获得了非常好的去斑效果.利用RADARSAT实测图像进行实验,并对实验结果作充分分析,证明了本算法的有效性.     

λ-WMAP: a statistical speckle filter operating in the wavelet domain

In this paper we introduce the Γ-WMAP filter, a wavelet based equivalent to the classical Γ-MAP filter. We model speckle as additive signal-dependent noise, and propose to use the normal inverse Gaussian (NIG) distribution as a statistical model for the wavelet coefficients of both the reflectance image and the noise image. A method for estimating the parameters of the proposed statistical models is presented, and we show that the NIG distribution makes excellent fits to the distributions of the wavelet coefficients of single-look synthetic aperture radar (SAR) images. The performance of the Γ-WMAP filter is tested on three single-look SAR images. We find that when the filter is used in a global mode it may severely blur the image. However, when applied in a local, adaptive mode the new algorithm has excellent de-speckling performance. Visual comparisons with the Γ-MAP filter show that Γ-WMAP tends to give better de-speckling. Quantitative comparisons in homogeneous regions using both the equivalent number of looks and the log standard deviation as measures definitely show that the Γ-WMAP gives better speckle filtering.     

侧扫声呐图像的3维块匹配降斑方法

斑点噪声是影响侧扫声呐图像质量的主要因素,降斑处理对侧扫声呐图像的判别与分析非常重要。针对侧扫声呐图像自身特性和斑点噪声分布特点,提出一种基于3维块匹配(BM3D)的降斑方法。根据海底散射模型,得到侧扫声呐图像斑点噪声的瑞利分布模型,然后通过高斯光滑函数幂变换将瑞利分布的噪声转化为高斯分布,通过对数变换将乘性噪声转变为加性噪声,再进行自适应的BM3D滤波,最后采用逆变换得到降斑图像。实验结果表明,该方法在降噪、边缘和纹理保持等方面均优于空间域、小波域、Curvelet域的一些降斑方法。     

一种改进的基于非局部均值的极化SAR相干斑抑制算法

贝叶斯形式的非局部均值模型在极化SAR图像相干斑抑制中有良好的应用,在实现抑制相干斑的同时较地保持了边缘细节和点目标。本文通过分析SAR图像多视数据的空间统计分布,结合贝叶斯形式的非局部均值模型,得出了在该模型下多视与单视SAR图像中像素间相似性度量函数一致性的结论,并对该相似性度量函数进行了修正,使之满足对称性;最后针对算法全局使用一个固定滤波参数影响滤波效果的问题,提出了一种根据像素间相似程度自适应选取滤波参数的方法。实验结果验证了本文算法的有效性。     

改进的基于非局部均值的极化SAR相干斑抑制

贝叶斯形式的非局部均值模型在极化SAR图像相干斑抑制中有良好的应用,在实现抑制相干斑的同时较好地保持了边缘细节和点目标.通过分析合成孔径雷达(SAR)图像多视数据的空间统计分布,结合贝叶斯形式的非局部均值模型,得出在该模型下多视与单视SAR图像中像素间相似性度量函数一致性的结论,并对该相似性度量函数进行了修正,使之满足对称性;最后针对算法全局使用一个固定滤波参数影响滤波效果的问题,提出一种根据像素间相似程度自适应选取滤波参数的方法.实验结果验证了本文算法的有效性.     

一种改进的SAR图像LEE滤波算法

针对SAR图像相干斑滤波中存在的降低相干斑与有效保持细节信息这一矛盾,研究了常用空域滤波算法,在此基础上,将中值滤波与增强LEE滤波相结合,改进了LEE滤波算法,该方法能够在滤除相干斑的同时很好地保持图像的边缘及细节纹理信息。     

基于惩罚系数自适应修正的SAR图像滤波新算法

合成孔径雷达(SAR)图像存在较强的相干斑点噪声,严重地影响了地物信息的提取与SAR图像的应用效果。提出了一种新的SAR图像斑点噪声滤波算法,该算法以一种基于膜模型的M arkov随机场的近似最优迭代滤波算法(TSPR)为基础,考虑了邻域空间关系对势能函数的影响,并通过在迭代过程中自适应修正惩罚系数,来达到更好的斑点噪声滤波效果。通过对含不同强度斑点噪声的退化图像的对比试验结果来看,该算法在提高处理后图像的信噪比方面,能够取得较TSPR算法更佳的效果。     

一种新型的空域SAR图像相干斑抑制方法

为了有效抑制SAR强度图像中的相干斑噪声,提出一种改进Sigma滤波并结合Gamma MAP滤波的空域相干斑抑制方法。首先利用阈值判断法判断并保留强点目标,然后结合SAR图像分布模型和MMSE准则判断Sigma区间,其中可以根据图像局部统计特性自适应调整窗口尺寸,最后选择Sigma区间内像素进行Gamma MAP滤波。实验结果表明:对于星载和机载SAR图像,在相干斑噪声抑制和边缘纹理细节信息保持方面,该方法较其他常用的空域相干斑抑制方法具有明显的优越性,能极大地提高SAR图像判读和目标识别能力。     

基于小波变换的SAR与可见光图像融合算法*

提出了一种基于小波变换的SAR图像与可见光图像的融合算法。为抑制斑点噪声,对SAR图像作平滑滤波。图像经小波变换后,计算不同分解层高频图像对应区域的均值与标准差,采用区域统计特性量测的融合规则;低频图像直接采用SAR图像的小波低频系数作为融合后的低频系数,对得到的融合低、高频图像作小波反变换。     

基于形态Haar小波的SAR图像斑点噪声抑制方法

针对现有相干斑抑制算法不能在去除斑点噪声和保持图像边缘、细节信息之间做到很好的折中,提出了一种新的基于形态Haar小波变换的合成孔径雷达(SAR)图像斑点噪声抑制方法。该方法首先对SAR图像进行二维形态Haar小波分解,图像的边缘、细节和纹理信息在低频子带中得到了更好的保留,噪声主要分布在高频子带;然后,根据各高频子带噪声的特点,分别对高频子带进行均值和中值滤波达到去除斑点噪声的目的;最后,再对低频子带和处理后的高频子带进行形态Haar小波精确重构得到去斑图像。实验证明:该算法不仅大大改善了原始SAR图像的画面质量,同时很好地保持了原始SAR图像的纹理特性和细节信息;该算法去斑性能指标总体优于传统的Lee滤波、Frost滤波、Kuan滤波和小波软阈值法。     

基于引导滤波和Tsallis熵的SAR图像分割算法

针对合成孔径雷达(SAR)图像易受噪声干扰、分割方法精度低的问题,提出了一种基于频域引导滤波和Tsallis熵的SAR图像多阈值分割算法.利用非下采样Contourlet变换(NSCT)对图像多尺度分解,提取图像各方向的高频信息;通过引导滤波增强高频分量的边缘信息,在保持边缘的同时抑制了相干斑噪声;利用改进的二维Tsallis熵多阈值对增强图像精确分割.实验结果表明:分割算法对噪声不敏感,分割精度和适应性明显提高.     

小波与双边滤波的医学超声图像去噪

目的：医学超声图像中的斑点噪声降低了图像质量并且限制了超声图像自动化诊断技术的发展。针对斑点噪声问题,提出了一种新型的基于小波和双边滤波的去噪算法。方法：首先,根据医学超声图像在小波域内的统计特性,在通用小波阈值函数的基础之上,改进了小波阈值函数。其次,将无噪信号的小波系数和斑点噪声的小波系数分别建模为广义拉普拉斯分布模型和高斯分布模型,利用贝叶斯最大后验估计方法得到了新型的小波收缩算法,利用小波阈值法对小波域内的高频信号分量进行去噪。最后,对小波域内的低频信号分量进行双边滤波处理,然后利用小波逆变换便得到去噪后的图像。结果：在仿真实验中,通过与其它7种去噪算法作对比,观察峰值信噪比(PSNR)等图像质量评价指标,结果表明本文算法的去噪效果优于其他相关算法。临床超声图像的实验结果进一步验证了本文算法的去噪性能。结论：本文提出了一种新型的去噪算法,实验表明本文算法能够很好地抑制斑点噪声,并且能保留图像病灶边缘等细节。     

SAR图像相干斑抑制研究进展

相干斑抑制是SAR图像处理领域的研究热点之一,也是SAR图像解译和应用中的关键步骤,因此SAR图像的相干斑抑制算法具有重要的研究价值。在简要介绍SAR图像相干斑的产生机理和数学模型的基础上,综述了国内外相干斑抑制的最新研究成果,重点分析了空域滤波和变换域滤波两类方法。从算法的可行性角度出发,分析了几种具有代表性的相干斑抑制方法及其优缺点,总结了常用相干斑抑制效果评价指标,最后对今后工作方向进行了展望。     

基于修正维纳滤波的小波包变换图像去噪

图像去噪是图像处理中一个非常重要的环节。为了改善降质图像质量,根据Donoho提出的小波阈值去噪算法,分析了维纳滤波原理,提出了一种基于修正维纳滤波的小波包变换图像去噪方法。利用修正维纳滤波对噪声图像进行处理,用处理后的图像计算噪声的标准方差,以此作为小波包的阈值。利用小波包对维纳滤波后的图像进行分解,实现对图像的低频和高频部分分别进行分解,用计算出的阈值对小波包树系数进行软阈值处理。利用小波包逆变换来获取去噪后的图像。结果表明：在噪声方差为0.01时,经该算法去噪后图像的PSNR比小波包自适应阈值去噪后的PSNR高出8.8 dB。该算法不仅能有效地去除加性高斯白噪声,而且能很好地保留边缘信息,极大地改善了图像的视觉质量。     

Comparison of different speckle-reduction techniques in SAR images using wavelet transform

Speckle noise is always present in Synthetic Aperture Radar (SAR) images. Many methods that reduce speckle noise while preserving texture and detail have been presented previously. In this paper, a comparison of different methods using wavelet decomposition is performed and new improvements for traditional methods are introduced. These techniques are: Wiener filtering, classical soft threshold, a new adaptive soft threshold and Bayesian reconstruction. First, speckle noise in a SAR image was analysed statistically. Then, a simulated image following these characteristics was created in order to evaluate noise reduction. The mean squared error was classified depending on the spatial characteristics of a local region. This tool gave valuable information for algorithm assessment. In the comparison, the new adaptive soft threshold method provided excellent results concerning noise reduction and detail preservation compared with classical soft threshold and Wiener methods. In addition, it gave as much noise reduction as the most sophisticated Bayesian method, but much more efficiently. Hence, the adaptive version of soft thresholding outperformed the other techniques. This study also presents a rigorous framework for speckle noise simulation and noise reduction evaluation.     

一种新的SAR目标及其阴影图像分割方法

针对相干斑对SAR图像分割质量的影响,给出一种新的SAR目标及其阴影图像分割方法。首先应用空域Wiener滤波器抑制棚干斑,然后根据滤波后图像统计特性确定分割阈值,对阈值分割结果进行形念学处理得到分割图像。利用MSTAR数据所作实验结果表明,采用有效的相干斑抑制方法可以更好地保持滤波图像中的目标,分割结果更为精细,并且算法速度快,有助提高ATR系统。     

SRAD-CNN for adaptive synthetic aperture radar image classification

The performance of synthetic aperture radar (SAR) image classification based on a conventional convolutional neural network (CNN) is limited by a trade-off between immunity to speckle noise and the ability to locate boundaries accurately. Difficulties regarding the accurate location of boundaries are a result of the smoothing effect of the pooling layer. To address this issue, we propose a novel framework called SRAD-CNN for SAR image classification. In this framework, we apply a filtering layer constructed according to prior knowledge of the speckle reducing anisotropic diffusion (SRAD) filter. The filtering layer can not only reduce speckle but also enhance the boundaries. The main parameter that controls the degree of filtering can be optimized adaptively by a backpropagation algorithm. Image patches adaptively filtered by the filtering layer are then put into the CNN layers to assign a label. Due to the effect of the filtering layer, for our proposed SRAD-CNN, both the speckle noise immunity and the sensitivity to boundaries are superior to those of conventional CNN.To confirm the performance of the proposed SRAD-CNN, we conducted experiments using both simulated and real SAR images. The experimental results demonstrated that the parameter of the filtering layer could be optimized adaptively for different scenes, different noise levels, and different image resolutions. The SRAD-CNN outperformed the conventional CNN in both overall classification accuracy and maintenance of boundary accuracy on images with different resolutions and noise levels with limited training samples.     

方向小波域的选择性阈值SAR图像去噪

SAR图像去噪一直是SAR图像处理中一个具有特殊意义的研究课题。噪声抑制的关键是解决图像平滑与保持纹理之间的矛盾。提出了一种基于方向小波的选择性阈值SAR图像去噪算法。该算法利用方向小波的多方向框架对图像作12个方向的分解和变换。针对方向小波分解图像所产生的系数序列长度不同的特点,利用白噪声的置信区间,将不同长度的系数分成3组,对中间长度的系数序列采用统一阈值,对其他长度序列采用白噪声置信区间阈值处理。为了更好地保持图像细节信息,将每一尺度高频系数的方差中值作为噪声方差估计值。利用真实的SAR图像进行去噪试验,与几种经典的空域滤波和小波软阈值算法进行比较结果表明,该算法在平滑图像的同时更好地保持了图像本身的纹理信息,图像的视觉效果优于其他算法,等效视数和边缘保持指数分别提高了97和0.15。     

基于稀疏重构框架下剪切波的SAR图像去噪

SAR图像很容易被乘性噪声多污染,进而影响SAR图像后序的分析与处理。本文中提出了一种基于剪切波稀疏编码的SAR图像移除乘性噪声的新模型。首先通过压缩感知理论建立SAR图像去噪模型;其次通过剪切波变换获得剪切波系数,每个尺度的系数视为一个单元;对于每个单元,通过剪切波域的贝叶斯估计对稀疏系数进行迭代估计。重现的单元最后结合起来构造去噪后的图像。SAR图像去噪效果显示了该算法有良好的表现性,对噪声具有鲁棒性;本文提出的算法不仅有较好的去噪效果,而且还保存了更多的边界信息。     

Application of special-purpose artificial neural networks for speckle reduction in SAR images

Synthetic aperture radar (SAR) is used extensively for remote-sensing applications due to its ability to operate under all weather conditions and provide high-resolution images. However, high-resolution images constructed from SAR data often suffer from speckle, which makes identification and classification of edges/boundaries a difficult task. Speckle noise is multiplicative in nature and is a result of constructive and destructive interference of signals from randomly distributed scatterers in a resolution cell illuminated by a coherent signal. Usually, speckle is reduced by incoherent averaging of high-resolution image pixels that degrade resolution. The principal goal in all speckle-reduction algorithms is to reduce speckle with minimum loss of resolution. In this investigation, we used specially trained and validated artificial neural networks (ANNs) for speckle reduction in images generated with a radar-depth sounder/imager and compared their performance to the conventional adaptive filtering and Speckle Reducing Anisotropic Diffusion (SRAD) algorithm. We show that by training different ANNs to reduce speckle noise at different levels of signal-to-noise ratio (SNR), rather than training one ANN to operate at all levels of SNR, improved performance in speckle reduction can be obtained. Real SAR images and synthetic noise are used in this research to compare the performance of the proposed ANN-based approaches with that obtained from conventional methods. This investigation shows that on combining the results from a set of properly trained and validated neural networks, the SNRs of the output images improve beyond those obtained from conventional approaches when the input SNRs are greater than or equal to 4 dB. For input SNRs greater than 0 dB, however, the ANNs provide better performance in edge preservation compared with conventional methods. We also found that once a set of ANNs is properly trained to reduce speckle from an image, these ANNs can be used in de-speckling other images without any further training. The merits and demerits of different configurations of the ANNs are studied to find useful speckle noise-tolerant ANN architectures.