Total-Variation-Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration

@article{He2016TotalVariationRegularizedLM,
  title={Total-Variation-Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration},
  author={Wei He and Hongyan Zhang and Liangpei Zhang and Huanfeng Shen},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2016},
  volume={54},
  pages={178-188}
}
In this paper, we present a spatial spectral hyperspectral image (HSI) mixed-noise removal method named total variation (TV)-regularized low-rank matrix factorization (LRTV). In general, HSIs are not only assumed to lie in a low-rank subspace from the spectral perspective but also assumed to be piecewise smooth in the spatial dimension. The proposed method integrates the nuclear norm, TV regularization, and L1-norm together in a unified framework. The nuclear norm is used to exploit the… CONTINUE READING

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