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Recently, the low-rank property of different components extracted from theimage has been considered in man hyperspectral image denoising methods.However, these methods usually unfold the 3D tensor to 2D matrix or 1D vectorto exploit the prior information, such as nonlocal spatial self-similarity(NSS) and global spectral correlation (GSC), which break the intrinsicstructure correlation of hyperspectral image (HSI) and thus lead to poorrestoration quality. In addition, most of them suffer from heavy computationalburden issues due to the involvement of singular value decomposition operationon matrix and tensor in the original high-dimensionality space of HSI. Weemploy subspace representation and the weighted low-rank tensor regularization(SWLRTR) into the model to remove the mixed noise in the hyperspectral image.Specifically, to employ the GSC among spectral bands, the noisy HSI isprojected into a low-dimensional subspace which simplified calculation. Afterthat, a weighted low-rank tensor regularization term is introduced tocharacterize the priors in the reduced image subspace. Moreover, we design analgorithm based on alternating minimization to solve the nonconvex problem.Experiments on simulated and real datasets demonstrate that the SWLRTR methodperforms better than other hyperspectral denoising methods quantitatively andvisually.

Hyperspectral Mixed Noise Removal via Subspace Representation and Weighted Low-rank Tensor Regularization