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Two-Step Sparse Decomposition for SAR Image Despeckling

机译：SAR图像去斑的两步稀疏分解

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In this letter, we propose a new despeckling method based on two-step sparse decomposition. First, the grouping by block matching method identifies similar image patches and stacks them into a group, so that the group of similar patches are mostly homogeneous, which is suitable for the followed sparse decomposition method. And then, the proposed two-step sparse decompositions are applied to each group. The first sparse decomposition is a classical sparse representation to obtain an overcomplete dictionary and the sparse coefficients. The second sparse decomposition is a subspace decomposition over the dictionary. We proposed a measurement from the sparse coefficients as the criterion to identify a principal signal subdictionary. Finally, the image is reconstructed by the linear combination of the atoms of the principal subdictionary. The proposed method takes benefits from learned overcomplete dictionary, which fully explores details and from the principal subdictionary, which reduces strong noises. Experimental results demonstrate the efficiency of the proposed method to denoise synthetic aperture radar images. Our method can achieve high performances in terms of both structure details preservation and speckle noise reduction.
机译：在这封信中，我们提出了一种新的基于两步稀疏分解的去斑点方法。首先，通过块匹配方法分组来识别相似的图像块并将它们堆叠为一组，从而使相似块的组大部分是同质的，这适用于随后的稀疏分解方法。然后，将提出的两步稀疏分解应用于每个组。第一个稀疏分解是获得稀疏字典和稀疏系数的经典稀疏表示。第二个稀疏分解是字典上的子空间分解。我们提出了一种基于稀疏系数的测量方法，以识别主要信号子类。最后，通过主要子类原子的线性组合来重建图像。所提出的方法得益于充分学习细节的学习过的完整字典以及主要字典，从而减少了强烈的噪音。实验结果证明了该方法对合成孔径雷达图像去噪的有效性。我们的方法在保留结构细节和减少斑点噪声方面都可以实现高性能。

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