2021
DOI: 10.48550/arxiv.2111.07044
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Hyperspectral Mixed Noise Removal via Subspace Representation and Weighted Low-rank Tensor Regularization

Abstract: Recently, the low-rank property of different components extracted from the image has been considered in many hyperspectral image denoising methods. However, these methods usually unfold the 3D tensor to 2D matrix or 1D vector to exploit the prior information, such as nonlocal spatial selfsimilarity (NSS) and global spectral correlation (GSC), which break the intrinsic structure correlation of hyperspectral image (HSI) and thus lead to poor restoration quality. In addition, most of them suffer from heavy comput… Show more

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