Blind hyperspectral image super resolution via simultaneously sparse and TV constraint

Abstract

This paper proposes a novel blind hyperspectral image super resolution method. The proposed method can estimate simultaneously the unknown hyperspectral image and the blur kernel based on the linear spectral unmixing technique. The total variation term is used for the blur kernel regularization and simultaneous total variation and sparse representation are used for abundance regularization terms. Because the image and blur kernel are simultaneously estimated with the double reg-ularization terms introduced for abundance, the estimation error can be minimized so that the performance of the proposed method can be improved. Finally, the proposed optimization formulation is effectively solved by block coordinate descent method. Experimental results show that the proposed method is effective and superior to existing blind hyperspectral image super resolution approach in terms of reconstruction quality.