HIGHLIGHTS
SUMMARY
In the recent decade, based on the spatial-spectral prior knowledge of HSI, numerous HSI restoration methods have been proposed. Inspired by this prior knowledge, many low-rank matrix or tensor-based methods have been proposed for HSI denoising. For instance, the low-rank matrix recovery (LRMR) approach reformulated the cubic HSI data into a low-rank matrix and adopted the GoDec algorithm to solve the restored problem. The weighted nuclear norm and total variation (WNNTV)-based HSI restoration approach was proposed to remove the hybrid noises of sparse noise and Gaussian noise. Of . . .
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