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首页> 外文会议>Conference on Algorithms and Systems for Optical Information Processing Ⅴ Jul 31-Aug 2, 2001, San Diego, USA >Spatially Adaptive Wavelet Transform Speckle noise smoothing Technique for SAR images
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Spatially Adaptive Wavelet Transform Speckle noise smoothing Technique for SAR images

机译：SAR图像的空间自适应小波变换散斑噪声平滑技术。

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摘要

In this work we propose a new wavelet transform based speckle denoising algorithm for SAR images. The algorithm will explicitly account for the signal dependent nature of the noise by studying the variances of detail wavelet coefficients. The algorithm will use the analysis of variance ANOVA technique to check if variances are due to means belonging to the same population or not. If neighboring variances indicate belonging to the same population, then it's a smooth region and coefficient should be smoothed. If neighboring variances indicate the presence of two different populations, then coefficient is due to image feature and should be preserved. This approach will provide the flexibility of adjusting to region intensity level and thus no need for the fixed threshold concept. The algorithm will take advantage of the fact that wavelet transform creates three detail sub-images and a coarse sub-image. Each detail sub-image is associated with frequency contents due to certain edge location and orientation. The algorithm will also consider using cross-information from all three-detail sub-images to decide whether coefficients are due to a feature and thus should be preserved, or they are due to noise and should be smoothed. Simulations will show that our algorithm will provide better performance in terms of PSNR, ENL, and visually than currently existing techniques.
机译：在这项工作中，我们提出了一种基于小波变换的SAR图像斑点去噪算法。该算法将通过研究细节小波系数的方差来明确考虑噪声的信号依赖性。该算法将使用方差分析方差分析技术来检查方差是否归因于属于相同总体的均值。如果相邻方差表明属于同一总体，则为平滑区域，应平滑系数。如果相邻方差表明存在两个不同的种群，则系数归因于图像特征，应予以保留。这种方法将提供适应区域强度水平的灵活性，因此不需要固定阈值概念。该算法将利用小波变换创建三个细节子图像和一个粗糙子图像这一事实。由于某些边缘位置和取向，每个细节子图像都与频率内容相关联。该算法还将考虑使用来自所有三个细节子图像的交叉信息来确定系数是否是由于某个特征引起的，因此应该保留，还是由于噪声而应该进行平滑。仿真将表明，与现有技术相比，我们的算法在PSNR，ENL和视觉方面都将提供更好的性能。

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