Hyperspectral Compressive Sensing Recovery via Spectrum Structure Similarity

In the hyperspectral compressive sensing reconstruction method, the exploitation of the prior information of the hyperspectral imagery can improve the reconstruction performance. As the existing methods have not taken into account the spectral structural redundancy information of hyperspectral imagery, a novel reconstruction method via spectrum structure similarity for hyperspectral compressive sensing is proposed in this paper. Structure images are proposed via spectrum structure similarity and a new regularizer is given based on structure images. It combines the new regularizer and other regularizers，so that the spatial redundancy, spectral statistical redundancy and spectral structural redundancy in hyperspectral imagery can all be exploited. In addition, an efficient solving algorithm based on variable-splitting is developed for the method. Experimental results show that the proposed method is able to reconstruct the hyperspectral imagery more efficiently than the current methods at the same measurement rates.