Low-rank tensor completion with spatial-spectral consistency for hyperspectral image restoration

Hyperspectral image (HSI) restoration has been widely used to improve the quality of HSI. HSIs are often impacted by various degradations, such as noise and deadlines, which have a bad visual effect and influence the subsequent applications. For HSIs with missing data, most tensor regularized methods cannot complete missing data and restore it. We propose a spatial-spectral consistency regularized low-rank tensor completion (SSC-LRTC) model for removing noise and recovering HSI data, in which an SSC regularization is proposed considering the images of different bands are different from each other. Then, the proposed method is solved by a convergent multi-block alternating direction method of multipliers (ADMM) algorithm, and convergence of the solution is proved. The superiority of the proposed model on HSI restoration is demonstrated by experiments on removing various noises and deadlines.