共查询到19条相似文献,搜索用时 718 毫秒
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基于静态小波分解的多尺度SAR图象滤波 总被引:2,自引:0,他引:2
由于雷达回波的相干性 ,合成孔径雷达 (SAR)图象上存在着斑点噪声 ,因此 ,为消除这种噪声 ,提出了一种基于静态小波分解的硬阈值滤波方法 ,该方法首先将 SAR图象分解至静态小波域 ,然后在静态小波域中将噪声的小波系数收缩至零 .将此算法应用于 ERS- 1SAR图象斑点噪声滤波 ,并与基于 Mallat分解的滤波算法和另外 3种典型的 SAR图象滤波算法进行比较 ,结果表明 ,该方法不仅可以有效地去除斑点噪声 ,并且可以保持 SAR图象的精细纹理结构 相似文献
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由于雷达回波的相干性,合成孔径雷达(SAR)图象上存在着斑点噪声,因此,为消除这种噪声,提出了一种基于静态小波分解的硬阈值滤波方法,该方法首先将SAR图象分解至静态小波域,然后在静态小波域中将噪声的小波系数收缩至零,将此算法应用于ERS-1 SAR图象斑点噪声滤波,并与基于Mallat分解的滤波算法和另外3种典型的SAR图象滤波算法进行比较,结果表明,该方法不仅可以有效地去除斑点噪声,并且可以保持SAR图象的精细纹理结构。 相似文献
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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。 相似文献
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SAR图像上周期性出现斑点噪声,影响图像的解译。小波变换具有多分辨分析特点。在分析SAR斑点噪声模型的基础上.利用小波变换方法对SAR图像斑点噪声进行抑制,同时给出噪声去除性能评价。实验结果表明,小波变换方法具有较好的斑点噪声去除性能。 相似文献
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SAR图像固有的斑点噪声严重影响了图像的判读和后续处理,因此抑制SAR图像斑点噪声显得尤其重要。一个良好的斑点噪声抑制算法应该在降低斑点噪声的同时,能很好地保持图像的细节特征,但现有的去噪算法没有一种能够完美地解决这个问题的。本文基于SAR图像斑点噪声滤波的重要性以及存在的问题,将整体变分偏微分方程用于去除斑点噪声。根据整体偏微分方程建立了去噪模型,并分析了模型的性能和参数选择的重要性。通过实验验证了该算法的有效性,并以峰值信噪比(PSNR)为评价准则,利用多项式拟合方法选择了最优参数。引入边缘保持指数(EPI),与其他滤波算法比较,本算法在去除噪声的同时较好地保持了边缘。 相似文献
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医学超声图像中固有的斑点噪声严重降低了图像的可解译程度,影响了后续的图像分析和诊断。提出了一种基于冗余小波变换的超声图像去斑算法,首先对含斑图像进行对数变换,将乘性噪声变成加性噪声;再对转换后图像做冗余小波分解;在小波系数服从广义高斯分布的前提下,计算每个小波高频子带的贝叶斯萎缩阈值,利用软阈值方法修正小波系数。实验结果表明,该算法去斑性能优于传统的空间域滤波和正交小波阈值去噪方法。 相似文献
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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。 相似文献
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Shiqi Huang WenZhun Huang Ting Zhang Cong Xu 《International journal of remote sensing》2016,37(23):5581-5604
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. 相似文献
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Weidong Yan Shaojun Shi Lulu Pan Gang Zhang Liya Wang 《International journal of remote sensing》2018,39(10):3055-3075
Change detection for synthetic aperture radar (SAR) images is a key process in many applications exploiting remote-sensing images. It is a challenging task due to the presence of speckle noise in SAR imaging. This article investigates the problem of change detection in multitemporal SAR images. Our motivation is to avoid using only one detector to measure the change level of different features which is usually considered by classical methods. In this article, we propose an unsupervised change detection approach based on frequency difference in wavelet domain and a modified fuzzy c-means (FCM) clustering algorithm. First, the proposed method extracts high-frequency and low-frequency components using wavelet transform, and then constructs high-frequency and low-frequency difference images using different detectors. Finally, inverse wavelet transform is carried out to obtain the final difference image. In addition, inspired by manifold structure constraint, we incorporate weighted local information into the FCM to reduce the influence of speckle noise. Experimental results performed on simulated and real SAR images show the effectiveness of the proposed method, in terms of detection performance, compared with the state-of-the-art methods. 相似文献
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JPEG2000是基于小波变换的图像压缩技术,它不像传统的压缩技术,JPEG2000的良好的压缩效果取决于选取好的小波.嵌入式零树小波编码算法是基于小波变换的一种图像压缩方法,也是有相对不错的压缩效果。对此研究将以嵌入式零树小波为基础,改进其算法,实现在航天项目上的快速高效的压缩图像。 相似文献
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相干斑噪声是SAR图像的固有特点。对相干斑抑制的要求是在平滑噪声的同时,尽量保持原始图像的结构信息。现有的许多相干斑抑制方法各有优点和不足,没有普遍的适用性。基于图像在小波域的隐马尔可夫模型(HMMs)结构,结合SAR图像中相干斑噪声的统计特性,本文提出了一种新的小波域相干斑抑制方法。仿真及实测数据处理结果表明,该方法在有效抑制相干斑的同时,更好地保持了边缘结构。与小波域软阈值去噪方法和Lee滤波器相比较,该方法在噪声平滑及边缘保持上都取得了较大的改进,并得到了较好的视觉效果。 相似文献
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多光谱遥感图像具有大数据量和高数据维的特点,如此海量的数据需要巨大的存贮空间。在研究嵌入式零树小波编码(EmbeddedZerotreeWavelet-EZW编码)的基础上,利用小波子带峰值的概念,通过对小波边缘子带进行有效处理,建立了改进的嵌入式零树小波编码(RenewedEmbeddedZerotreeWavelet-REZW编码)快速算法。采用改进的EZW编码算法,对遥感图像进行了压缩,获得了具有较高压缩比和较好视觉效果的重建图象,证明了REZW算法的可行性和有效性。 相似文献