Hyperspectral image low-rank restoration based spectral-spatial total variation

Hyperspectral image (HSI) is often contaminated by mixed noise in the acquisition process. In this paper, a hyperspectral image low-rank restoration method based spectral-spatial total variation (LRSSTV) is proposed. The spectral high correlation is exploited by low-rank representation and the sparse noise is represented by the /i-norm. Furthermore, to remove the Gaussian noise and enhance the edge information, spectral-spatial adaptive total variation prior knowledge is utilized. Both simulated and real-world data experimental results show that the proposed method can work well in detail preservation and noise removal.