Compressive Hyperspectral Imaging With Spatial and Spectral Priors

This paper proposes a new compressive hyperspectral imaging method, including the design of a cost-effective distributed sampling (DS) scheme and an efficient reconstruction model. The new sampling scheme, named as distributed separate sampling (DSS), encodes different hyperspectral bands with mutually independent two-dimensional separate sensing operators. Compared with existing DS schemes, DSS reduces lots of resource overhead in the premise of generating measurements with low redundancy. Furthermore, in contrast to the existing DS schemes, DSS keeps the original structure of hyperspectral images (HSIs) during sampling procedure. The new joint reconstruction model, namely, joint nuclear/total variation/L₁ norm minimization, exploits both spatial and spectral priors of HSIs. Unlike the other joint reconstruction models, the proposed model utilize an L₁-based distance function to measure the similarity between adjacent bands, which improves the recovery quality of HSIs. Besides, we develop a new compressive inversion algorithm under the split Bregman framework, which is of low computational complexity, to solve our proposed reconstruction model. Comprehensive experimental results demonstrate the efficiency of our method.