Noise Removal From Hyperspectral Images by Multidimensional Filtering
Noise Removal From Hyperspectral Images by Multidimensional Filtering
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DOI：
10.1109/tgrs.2008.916641
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发表时间：
2008-06
影响因子：
8.2
通讯作者：
Damien Letexier;S. Bourennane
中科院分区：
文献类型：
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作者：
Damien Letexier;S. Bourennane
提出了一种适用于高光谱图像的广义多维维纳滤波去噪方法。通常，多维数据过滤是基于数据矢量化或矩阵化的。很少有人提出新的方法来处理多维数据。多维维纳滤波(MWF)就是其中的一种。它将多维数据集视为三阶张量。它还依赖于信号子空间和噪声子空间之间的可分性。利用多线性代数，MWF需要展平张量。但是，展平始终是垂直执行的，这可能不适用于数据。事实上，作为一种基于Tucker的滤波，MWF只考虑有用的信号子空间。当信号子空间和噪声子空间非常接近时，很难提取出所有有用的信息。这可能会导致恢复的HSI中出现伪影和空间分辨率损失。我们提出的方法估计了张量展平的相关方向，这些方向可能既不平行于行，也不平行于列。当重新排列数据以使得可以在估计的方向上执行平坦化时，降低了信号子空间维度，并且提高了信噪比。我们采用了二维直线检测算法来估计HSI的主方向，这些方向被用来展平HSI张量。我们还将四叉树划分推广到张量，以适应图像不连续的滤波。与MWF、小波阈值和逐通道维纳滤波的比较研究表明，我们的算法在恢复受损的HYDICE HSI时提供了更好的性能。
A generalized multidimensional Wiener filter for denoising is adapted to hyperspectral images (HSIs). Commonly, multidimensional data filtering is based on data vectorization or matricization. Few new approaches have been proposed to deal with multidimensional data. Multidimensional Wiener filtering (MWF) is one of these techniques. It considers a multidimensional data set as a third-order tensor. It also relies on the separability between a signal subspace and a noise subspace. Using multilinear algebra, MWF needs to flatten the tensor. However, flattening is always orthogonally performed, which may not be adapted to data. In fact, as a Tucker-based filtering, MWF only considers the useful signal subspace. When the signal subspace and the noise subspace are very close, it is difficult to extract all the useful information. This may lead to artifacts and loss of spatial resolution in the restored HSI. Our proposed method estimates the relevant directions of tensor flattening that may not be parallel either to rows or columns. When rearranging data so that flattening can be performed in the estimated directions, the signal subspace dimension is reduced, and the signal-to-noise ratio is improved. We adapt the bidimensional straight-line detection algorithm that estimates the HSI main directions, which are used to flatten the HSI tensor. We also generalize the quadtree partitioning to tensors in order to adapt the filtering to the image discontinuities. Comparative studies with MWF, wavelet thresholding, and channel-by-channel Wiener filtering show that our algorithm provides better performance while restoring impaired HYDICE HSIs.