Super-resolution hyperspectral imaging with unknown blurring by low-rank and group-sparse modeling

When the system blurring is unknown, we propose a novel super-resolution approach of hyperspectral images by low-rank and group-sparse modeling. No high spatial resolution auxiliary data or prior information about blurring isna needed. The proposed method imposes the low-rank model with predefined spectral subspace and group sparse model on different types of high frequency components to take advantage of the shared spatial structure across all spectral bands. The desired high spatial resolution hyperspectral image and blurring kernel are optimized alternatively according to the proposed cost function. Experimental results demonstrate the effectiveness and stability of the proposed method in practical applications.