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The images of synthetic aperture radar(SAR) have been widely used in many important fields.Like any coherent imaging system,SAR systems are subject to speckle effects.This noise is conspicuous in single_look SAR images which disturb image interpretation and target classification.Therefore,to extent the use of SAR images,effective speckle reduction techniques are highly desirable. Based on the principle of multiresolution wavelet analysis,a new filter for suppression speckle in SAR is proposed.For images,an...

The images of synthetic aperture radar(SAR) have been widely used in many important fields.Like any coherent imaging system,SAR systems are subject to speckle effects.This noise is conspicuous in single_look SAR images which disturb image interpretation and target classification.Therefore,to extent the use of SAR images,effective speckle reduction techniques are highly desirable. Based on the principle of multiresolution wavelet analysis,a new filter for suppression speckle in SAR is proposed.For images,an algorithm similar to the one_dimensional case is possible for two_dimensional wavelets and scaling functions obtained from one_dimensional ones by tensorial product.This kind of discrete two_dimensional wavelet transform leads to a decomposition of approximation coefficients at level j+1 in three components: the approximation at level j and the details in three orientations (horizontal,vertical,and diagonal).The “approximate image" at level j is composed of the low frequency parts in both row and column directions of approximate image at level j+1 .And the there “detail images" contains its high frequency components separately in horizontal,vertical,diagonal directions.The approximate image is iteratively decomposed into four sub_images level by level.A pyramidal wavelet decomposing structure is thus constructed.It is tes_tified that the subspaces level of 4 or 5 is adequate for multiresolution wavelet analysis of an image (1024 ×1024).At each stage of the multiresolution pyramid,the approximate signal is low_pass filtered and decimated.And the wavelet coefficients are computed by high_pass filtering.It is proved that the signal energy concentrates on the wavelet coefficients of higher absolute value,and that the coefficients of high absolute value is more valuable than that of low absolute value in the signal wavelet reconstruction.It is also demonstrated that the effect of speckle is more serious in higher frequency parts,and the speckle decrease sharply as the image is low_filtered.So it can be considered that the speckle in 4 or 5 level approximate image can be ignored.The detail images of each subspace in which the ratio of signal to speckle is comparatively low are processed.The speckle of detail image at high frequency level is suppressed comparatively more by soft_threshold technique while the speckle at low frequency is suppressed comparatively less.The filter's threshold is computed according to the number of level,the variance and the length of each detail images. The result image which is reconstructed by the processed detail images of each level is estimated through the effect of vision,the quantitative analysis such as mean,standard and variance,and the analysis of preservation of edge and detail.The filtering results of ERS_1 SAR image and image with simulated speckle show that the method proposed in this paper is satisfying in visual appearance,speckle suppression and detail preservation.

利用多分辨率小波分析的理论 ,分析了SAR图像经多分辨率小波分解后生成的系列子图像中信号与斑点噪声能量分布特性及其信噪比的变化规律 ,提出了一种新的小波域斑点噪声的滤波算法 ,该滤波算法的阈值取决于各细节子图像的序列长度、方差及其所在的层次 ,并采用真实SAR数据和模拟加噪图像进行了试验。结果表明 ,该算法具有较强的噪声抑制和较好的边缘、细节保护能力及目视效果

A Method of image denoising based on multiwavelet transform and multilevel threshold is proposed.Firstly,noised image are decomposed by multiwavelet transform,Secondly,in high frequency area with different scale,the coefficients are dealed with different threshold according to coefficients energy distribution.Finally,reconstructed image can be obtained by using the inverse multiwavelet transform for all important coefficients and approximation coefficients.Experimental results prove that by using this method,image...

A Method of image denoising based on multiwavelet transform and multilevel threshold is proposed.Firstly,noised image are decomposed by multiwavelet transform,Secondly,in high frequency area with different scale,the coefficients are dealed with different threshold according to coefficients energy distribution.Finally,reconstructed image can be obtained by using the inverse multiwavelet transform for all important coefficients and approximation coefficients.Experimental results prove that by using this method,image noise can be reduced effectively and image details can be preserved a lot.

提出了一种基于多小波变换分层阈值的图像降噪的方法。此方法首先对一幅噪声图像进行多小波分解;接着根据多小波分解后的能量分布特性,在不同尺度的高频子带内,对小波系数进行不同阀值处理;最后经多小波反变换,得到重构图像。实验结果表明:此方法既可以有效地降低噪声,又可以较好地保持图像细节。

For keeping image detail and constraining image noise, traditional filters are mostly those in space domain or frequency domain. In space domain, we can more effectively constrain noise while it blurs image details. So, the filters in frequency domain have attracted more and more attention. In wavelet frequency domain, image frequency can be effectively decomposed and then noise can be restricted. Traditionally, we make use of Donoho's threshold to de-noise and preserve image details with regard to...

For keeping image detail and constraining image noise, traditional filters are mostly those in space domain or frequency domain. In space domain, we can more effectively constrain noise while it blurs image details. So, the filters in frequency domain have attracted more and more attention. In wavelet frequency domain, image frequency can be effectively decomposed and then noise can be restricted. Traditionally, we make use of Donoho's threshold to de-noise and preserve image details with regard to wavelet coefficients. However, which also results in blur image details etc. It is known that wavelet transformation has good performance in local time domain and frequency domain. Sub-images are acquired by multilevel wavelet transformations, then we can find that wavelet coefficients own similarity of energy distribution in high frequency sub-images, that is to say, wavetlet coefficients distribute much wider through increasing scale of decomposition. Sub-images of lower resolution whose wavelet coefficients own wider range, sub-images of higher resolution whose wavelet coefficients own narrower range. Therefore, we present a new wavelet de-noising algorithm based on characteristic of energy distribution.

目前对于保持图像细节、滤除噪声,普遍采用空间域、频率域滤波.在空间域滤波,尽管能够有效地限制噪声,但是同时模糊了图像细节.因此,在频率域滤波的方法越来越引起关注.在小波频率域中,我们常常采用Donoho阈值方法处理小波系数来以此去除噪声,保留图像细节,然而该方法同时也一定程度上模糊了图像细节.小波变换具有良好的时、频局部化性能,图像经过多级小波变换得到不同分辨率的子图个数,各高频子图上的小波系数具有相似的能量统计分布特性.也就是说随着分解层数的增加,分辨率最低子图的小波系数范围最大,而高分辨率子图上大部分数值接近于0.因此,该文提出了一种新的基于能量分布特性的小波去噪算法(WCED).

 
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