Laplacian Regularized Tensor Low-Rank Minimization for Hyperspectral Snapshot Compressive Imaging

Abstract

Snapshot Compressive Imaging (SCI) systems, including hyperspectral compressive imaging and video compressive imaging, are designed to depict high-dimensional signals with limited data by mapping multiple images into one. One key module of SCI systems is a high quality reconstruction algorithm for original frames. However, most existing decoding algorithms are based on vectorization representation and fail to capture the intrinsic structural information of high dimensional signals. In this paper, we propose a tensor-based low-rank reconstruction algorithm with hyper-Laplacian constraint for hyperspectral SCI systems. First, we integrate the non-local self-similarity and tensor low-rank minimization approach to explore the intrinsic structural correlations along spatial and spectral domains. Then, we introduce a hyper-Laplacian constraint to model the global spectral structures, alleviating the ringing artifacts in the spatial domain. Experimental results on hyperspectral image corpus demonstrate the proposed algorithm achieves average 0.8~2.9 dB improvement in PSNR over state-of-the-art work.