Bi-Level Tensor Decomposition for Hyperspectral Image Restoration

This paper proposes a bi-level tensor decomposition (BLTD), properly exploiting the characterization advantages of tensor subspace representation (TSR) and tensor ring decomposition (TRD). More specifically, the first level is related to the decomposition of a third-order tensor into its TSR using the tensor-tensor product (t-product), and the second level performs TRD on the coefficient tensor obtained by TSR. Leveraging the proposed BLTD, we design a bi-level tensor nuclear norm (BLTNN)-based model for hyperspectral images (HSIs) denoising. To solve the model, we develop an efficient alternating direction method of multipliers (ADMM)-based algorithm. Experimental results demonstrate the superior performance of our method compared to existing methods.