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

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 L 1 -norm together in a unified framework. The nuclear norm is used to exploit the spectral low-rank property, and the TV regularization is adopted to explore the spatial piecewise smooth structure of the HSI. At the same time, the sparse noise, which includes stripes, impulse noise, and dead pixels, is…

• NN
  National Natural Science Foundation of China

  Awards: 41431175, 61201342
• NK
  National Key Research and Development Program of China

  Award: 2011CB707105