Spatial-Spectral Total Variation Constrained Collaborative Tensor Regularization for Dual-Camera Compressive Hyperspectral Imaging

In this paper, we propose a novel tensor-based approach to improve the reconstruction performance for dual-camera compressive hyperspectral imaging. We formulate a coupled tensor decomposition model to maintain the consistency of the spatial structure of the HSI and the panchromatic image. We introduce a regularizer over the core tensor to collaboratively promote global spatial-spectral correlations in HSI. Besides, we incorporate an anisotropic spatial-spectral total variation (SSTV) regularization to characterize the piecewise smooth structure of the HSI. Then the alternating direction method of multipliers (ADMM) algorithm is applied to the optimization problem. Experimental results on a public dataset demonstrate the superiority of the proposed approach.