Adaptive Dimension-Discriminative Low-Rank Tensor Recovery for Computational Hyperspectral Imaging

Exploiting the prior information is fundamental for image reconstruction in computational hyperspectral imaging (CHI). Existing methods usually unfold the 3D signal as a 1D vector and then handle the prior information among different dimensions in an indiscriminative manner, which inevitably ignores the high-dimensionality nature of the hyperspectral image (HSI) and thus results in poor reconstruction performance. In this paper, we propose a high-order tensor optimization based reconstruction method to boost the quality of CHI. Specifically, we first propose an adaptive dimension-discriminative low-rank tensor recovery (ADLTR) model to exploit the high-dimensionality prior of HSI faithfully. In the ADLTR model, we utilize the 3D tensors as the basic elements to fundamentally preserve the structure information in the spatial and spectral dimensions, introduce a dimension-discriminative low-rankness model to fully characterize the prior in the basic elements, and propose a weight estimation strategy by adaptively exploiting the diversity in each dimension. Then, we develop an optimization framework for the CHI reconstruction by integrating the structure prior in ADLTR with the system imaging principle, which is finally solved via the alternating minimization scheme. Extensive experiments on both synthetic and real data demonstrate that our method outperforms state-of-the-art methods.