SAR Image Filtering Via Learned Dictionaries and Sparse Representations
SAR Image Filtering Via Learned Dictionaries and Sparse Representations
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DOI：
10.1109/igarss.2008.4778835
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发表时间：
2008-07
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影响因子：
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通讯作者：
S. Foucher
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文献类型：
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作者：
S. Foucher
在过去的十年里，人们对信号的稀疏表示的研究越来越感兴趣。特别是在几何空间中，许多新的多尺度图像表示被提出（Curvelets, Ridgelets， Contourlets等）。与使用固定变换不同，另一种方法是从信号本身构建一个稀疏字典。在目前的工作中，我们提出了一种新的方法来减少SAR图像中的斑点噪声，该方法使用训练字典上的稀疏和冗余表示。在这种方法中，从图像中学习由图像补丁（称为原子）组成的自适应字典，以便它构成图像内容的稀疏表示。这种学习过程被称为K-SVD，使用正交匹配追踪（OMP）和奇异值分解（SVD）有效地完成。这种新方法在去除白色加性高斯噪声方面是有效的，尽管字典中的元素是从有噪声的图像中学习的，但该算法正在向已经显示噪声水平降低的有意义的原子收敛。
In the last decade there has been a growing interest in the study of sparse representation of signals. In particular, many new multiscale image representations in a geometric space have been proposed (Curvelets, Ridgelets, Contourlets, etc.). Instead of using a fixed transformation, an alternative approach is to build a sparse dictionary from the signal itself. In the present work, we propose a novel approach for speckle noise reduction in SAR images using a sparse and redundant representation over trained dictionaries. In this approach, an adaptive dictionary composed of image patches (called atoms) is learned from the image so that it constitutes a sparse representation of the image content. This learning process, called K-SVD, is efficiently performed using an Orthogonal Matching Pursuit (OMP) and a Singular Value Decomposition (SVD). This new approach is effective in removing white additive Gaussian noise despite the fact that elements of the dictionary are learned from the noisy image, the algorithm is converging toward meaningful atoms that are already showing a reduction in noise level.