极化SAR相干斑抑制的非局部加权最小均方误差滤波算法

目的 相干斑的存在严重影响了极化合成孔径雷达(PolSAR)的影像质量.对相干斑的抑制是使用SAR数据的必不可少的预处理程序.提出一种基于非局部加权的线性最小均方误差(LMMSE)滤波器的极化SAR滤波的方法.方法 该方法的主要过程是利用非局部均值的理论来获取LMMSE估计器中像素样本的权重.同时,在样本像素的选取过程中,利用待处理像素的极化散射特性和邻域块的异质性来排除不相似像素以加速算法,同时达到保持点目标和自适应调节块窗口大小的目的.结果 模拟影像和真实影像上进行的实验结果表明,采用这种方法滤波后影像的质量得到明显改善.和传统的LMMSE算法相比,无论是单视的影像还是多视的影像,本文方法去噪结果的等效视数都高出8视以上;峰值信噪比也提升了5.8 dB.同时,去噪后影像分类的总体精度也达到了83％以上,该方法的运行效率也比非局部均值算法有了较大提升.结论 本文方法不仅能够有效抑制相干斑噪声,还能较好地保持边缘和细节信息以及极化散射特性.这将会为后续高效利用SAR数据提供保障.     

基于双窗口和极值压缩的自适应中值滤波

为了提高中值滤波器的滤波性能和适应不同密度的椒盐脉冲噪声,从噪声检测的准确性、噪声滤除的有效性和滤波速度的实用性等3个方面分别对中值滤波方法加以改进,提出了一种基于双窗口和极值压缩的自适应中值滤波方法(DWECAMF)。该方法采用大窗口检测噪声和小窗口滤除噪声的滤波策略、压缩噪声滤除窗口内极大值和极小值策略以及自适应脉冲噪声滤除策略,以提高图像滤波性能,同时采用了移动滤波策略提高滤波速度以增强其实用性。实验表明,该方法在以上3个方面的性能都有极大提高,并且对不同密度的椒盐噪声都具有很好的滤波性能。     

A statistical and Wiener filtering algorithm based on the curvelet transform for SAR images

Synthetic aperture radar (SAR) images contain many kinds of noise. Speckle noise is multiplicative noise generated by the coherent imaging processes involved in SAR images and brings a great hindrance to the interpretation and application of SAR images, so it is considered the first major kind of noise in SAR images. SAR images also contain other incoherent additive noises generated by other factors, such as Gaussian noise, which are all considered the second major kind of noise. In order to reduce the impact of noise as much as possible, after an in-depth study of SAR imaging and noise-generating mechanism, curvelet transform principle, and Wiener filtering characteristic, a novel filtering method, here called the statistical and Wiener based on curvelet transform (SWCT) method is proposed. The SWCT algorithm processes two different kinds noise based on their properties. Specifically, it establishes a two-tiered filtering framework. For the first kind of noise, the algorithm uses the curvelet transform to decompose the SAR image and uses the statistical characteristics of the SAR image to generate an adaptive filtering threshold of the coefficients of decomposition to recover the original image. Then it filters every sub-band image at each decomposed scale and performs the inverse curvelet transform. The second kind of noise is directly filtered using the Wiener filter in the SWCT algorithm. Using the two-tiered filtering model and fully exploiting statistical characteristics, the SWCT algorithm not only reduces the amount of coherent speckle noise and incoherent noise effectively but also retains the edges and geometric details of the original SAR image. This is very good for target detection, classification, and recognition. Qualitative and quantitative tests were performed using simulated speckle noise, Gaussian noise, and real SAR images. The proposed SWCT algorithm was found to remove noise effectively and the performance of the algorithm was tested and compared to the mean filter, enhanced gamma-MAP (maximum a posterior probability) filter, wavelet transform filter, Wiener filter, and curvelet transform filter. Experiments carried out on real SAR images confirmed that the new method has a good filtering effect and can be used on different SAR images.     

一种基于多尺度噪声检测的图像中值滤波器

介绍了标准中值滤波与有效中值滤波的概念,提出了一种基于自适应多尺度噪声检测的中值滤波器,可用于恢复被椒盐脉冲噪声污染了的图像。滤波器将输入图像像素分为有效信号类、脉冲噪声类和恒定区域类,对各类像素采用不同的方法进行滤波处理。实验结果证明,本文算法的性能比现存的其它许多算法有了显著的提高,而且便于实现。     

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。     

高阶PDE模型中的松弛中值图像去噪方法

针对平均曲率极小化模型在去噪过程中产生斑点的问题,提出了一种平均曲率和松弛中值滤波结合的迭代算法。首先,使用平均曲率模型对噪声图像处理,根据局部方差信息,利用阈值确定斑点的位置。其次,利用具一定边界保持性质的松弛中值滤波器消除斑点噪声。最后,为更有效地消除斑点,在每一次随着时间的迭代后都使用松弛中值滤波。对曲线和图像进行去噪仿真实验,结果表明,相对于平均曲率模型,本文算法在客观指标和主观视觉效果上均有更好的去噪效果和更低的时间复杂度。     

The effect of median filtering on synthetic aperture radar images

Synthetic aperture radar (SAR) images are subject to intrinsic 'noise', called speckle, over and above any spatial variability due to variations in the properties of the scene. Many noise-reduction techniques have been employed to reduce the effects of this phenomenon. In this note we review the statistical effects of one of the simplest such techniques, the median filter. This filter can be performed almost as rapidly as the mean (box average) filter but has significantly better edge-preserving properties. It is, however, unsuited to images containing significant point- or small-target features. Use of the median filter can introduce significant biases into the data, for example a 25 per cent reduction in an intensity image after 3 by 3 median filtering. This note presents calculations of the size of these biases for the case of homogeneous target areas, fully-developed speckle, and statistically independent looks in multi-look images.     

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.     

基于自适应开关插值算法的图像椒盐噪声滤波*

针对传统中值滤波算法在滤除椒盐噪声时的缺点,提出了一种自适应开关插值算法。该方法根据椒盐噪声的特点,通过极大值、极小值和块均匀度检测来标志噪声,然后根据噪声分布情况,利用拉格朗日插值和自适应中值滤波来滤除噪声。实验结果表明,该方法对椒盐噪声密度为10%80%的测试图像,能更加有效地抑制椒盐噪声并很好地保持了图像的细节信息,滤波性能比传统中值滤波方法更理想。该方法为图像去噪提供了一种新的途径。     

基于粒子群算法的图像椒盐噪声去除算法

针对图像中的椒盐噪声消除问题,提出了一种基于粒子群算法的自适应开关中值滤波算法。提出的滤波器算法主要由两大阶段组成:噪声检测阶段和噪声滤除阶段。与标准中值滤波相比,提出的自适应开关中值滤波算法能够生成污染图像的噪波图。通过噪波图可以得到图像的污染和未污染像素信息。在滤除过程中,滤波器计算出未污染相邻像素的中值并且替换污染像素。仿真实验结果证实了所提算法的有效性,其能够有效地提高图像的峰值信噪比和图像质量;相比现有其他方法,所提算法的去噪效果更好。     

An adaptive filter for speckle suppression in synthetic aperture radar images

Synthetic aperture radar images are generally corrupted by speckle noise. This arises due to the coherent nature of radar echoes used in the image formation and it is often necessary to enhance the image by speckle suppression before data can be used in various applications. To suppress speckle and improve the radar image interpretability a simple filtering technique has been proposed. The filter is adaptive to the variance of pixel intensity in a sliding window and accordingly decides the number of nearest neighbours to the central pixel to replace its intensity with the average intensity of those nearest neighbours. The performance of the filter has been studied for speckle removal in the homogeneous areas and its edge retention capability and compared with some of the widely known speckle filters. The results show that the proposed filter retains edges, removes speckle noise and compares well with other known filters in the literature.     

一种倒数加权的窗口自适应邻域图像滤波算法

对于叠加了白噪声的图像,提出一种倒数加权的窗口自适应邻域图像滤波算法.算法首先利用自适应邻域统计的概念在以每个滤波点为中心的滤波窗口内,为其建立参与滤波的自适应邻域像素集合,随后对纳入自适应邻域的像素进行倒数加权作为滤波结果.仿真和对比实验的结果显示,提出的算法在性能上超过了传统的中值滤波和窗口自适应邻域滤波算法.它能在有效抑制加性白噪声的同时,完好地保持图像的边界和细节信息,并且滤波后的图像获得了良好的视觉效果.     

自适应滤波结合改进T-snake模型的甲状腺超声图自动分割方法

提出一种基于T-snake模型的甲状腺超声波图像分割的新方法。首先,结合基于窗口的各向异性扩散滤波方法与自适应加权中值滤波算法有效地消除甲状腺超声波图像斑点噪声;其次,以传统T-snake模型为基础,增加自适应区域能量和膨胀力对非连续边界与弱边界进行有效提取,实现甲状腺超声波图像的自动分割;最后设定模型参数,使用临床数据进行实验。结果证明,应用该方法得到自动分割结果的平均相对差异度小于5%,平均相对重叠度大于91%,验证了其可行性。     

结合暗通道先验的光补偿快速去雾算法

针对暗通道先验单幅图像去雾算法去雾不彻底、天空区域偏色严重且去雾速度慢等问题,提出了一种结合暗通道先验的光补偿快速去雾算法。首先将二阶Butterworth高通滤波器引入同态滤波函数,在频域内对最小颜色分量进行增强,同时,平滑最小颜色分量中的光照,补偿局部区域因光照不足引起的图像质量下降;然后用双边滤波对其进行平滑处理,使光照在最小颜色分量图像上过渡更加自然;最后将处理之后的最小颜色分量作为引导图细化初始透射率。实验结果表明,与Tarel算法和中值滤波算法相比,该算法得到的去雾图像具有更好的视觉效果;与引导滤波算法相比,该算法去雾效果更为彻底,天空区域颜色还原准确,且运算速度更快。     

基于混沌优化的自适应中值滤波

提出了用混沌优化的方法进行自适应中值滤波。该滤波是在自适应中值滤波的基础上,将混沌优化与E-中值滤波结合起来输出最佳值。仿真结果表明,这种中值滤波不仅去噪效果较好,而且对噪声污染严重的图像也能很好地保护图像的细节。     

结合修剪均值与高斯加权中值滤波的图像去噪算法

为快速准确地滤除图像中的脉冲噪声并较好地保持图像的纹理细节和边缘结构,提出一种基于修剪均值与高斯加权中值滤波的图像去噪算法。根据脉冲噪声的灰度特征与统计特征,以局部统计方式进行噪声检测,将灰度取最小值或最大值且与邻域像素相关性较小的像素识别为噪声像素。对于图像平滑区域和细节区域中的噪声像素,使用自适应修剪均值和高斯加权中值滤波算法进行去噪处理。实验结果表明,该算法在视觉效果、峰值信噪比、结构相似性及计算速度上均优于对比算法,并且能够在彻底滤除噪声的同时,较好地保持图像的纹理细节和边缘结构。     

Adaptive Unsymmetrical Trim-Based Morphological Filter for High-Density Impulse Noise Removal

The modified decision-based unsymmetrical trimmed median filter (MDBUTMF), which is an efficient tool for restoring images corrupted with high-density impulse noise, is only effective for certain types of images. This is because the size of the selected window is fixed and some of the center pixels are replaced by a mean value of pixels in the window. To address these issues, this paper proposes an adaptive unsymmetrical trim-based morphological filter. Firstly, a strict extremum estimation approach is used, in order to decide whether the pixel to be processed belongs to a monochrome or non-monochrome area. Then, the center pixel is replaced by a median value of pixels in a window for the monochrome area. Secondly, a relaxed extremum estimation approach is used to control the size of structuring elements. Then an adaptive structuring element is obtained and the center pixel is replaced by the output of constrained morphological operators, i.e., the minimum or maximum of pixels in a trimmed structuring element. Our experimental results show that the proposed filter is more robust and practical than the MDBUTMF. Moreover, the proposed filter provides a preferable performance compared to the existing median filters and vector median filters for high-density impulse noise removal.

     

基于噪声检测和动态窗口的图像去噪算法

针对中值滤波算法在去除脉冲噪声时易造成图像细节丢失的问题,提出了一种基 于噪声检测和动态窗口的自适应滤波方法。首先借鉴 BDND 方法,将图像的像素初分成信号点 和疑似噪声点,以减少需要处理的像素点;然后设计一种窗口自适应的噪声检测方法对疑似噪 声点进一步检测,判断其是真噪声点还是细节点,以加强图像细节信息的保护;最后通过改进 的自适应中值滤波器滤除检测出的噪声,并融入窗口自适应控制,窗口的大小可以根据噪声情 况自适应地调整,在去除噪声的同时尽可能地保护图像细节。实验表明,该算法在噪声处理和 细节保护上要优于其他典型算法,能有效地提高图像的峰值信噪比,对于高密度噪声的图像, 也可以获得较好的去噪效果。     

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。     

SAR image processing using adaptive stack filter

Stack filters are a special case of non-linear filters. They have a good performance for filtering images with different types of noise while preserving edges and details. A stack filter decomposes an input image into several binary images according to a set of thresholds. Each binary image is filtered by a Boolean function. The Boolean function that characterizes an adaptive stack filter is optimal and is computed from a pair of images consisting of an ideal noiseless image and its noisy version. In this work the behavior of adaptive stack filters on synthetic aperture radar (SAR) data is evaluated. With this aim, the equivalent number of looks for stack filtered data are calculated to assess the speckle noise reduction capability of this filter. Then a classification of simulated and real SAR images is carried out on data filtered with a stack filter trained with selected samples. The results of a maximum likelihood classification of these data are evaluated and compared with the results of classifying images previously filtered using the Lee and the Frost filters.