High resolution hyperspectral images have important applications in many areas, such as anomaly detection, target
recognition and image classification. Due to the limitation of the sensors, it is challenging to obtain high spatial
resolution hyperspectral images. Recently, the methods that reconstruct high spatial resolution hyperspectral images
from the pair of low resolution hyperspectral images and high resolution RGB image of the same scene have shown
promising results. In these methods, sparse non-negative matrix factorization (SNNMF) technique was proposed to
exploit the spectral correlations among the RGB and spectral images. However, only the spectral correlations were
exploited in these methods, ignoring the abundant spatial structural correlations of the hyperspectral images. In this
paper, we propose a novel algorithm combining the structural sparse representation and non-negative matrix
factorization technique to exploit the spectral-spatial structure correlations and nonlocal similarity of the hyperspectral
images. Compared with SNNMF, our method makes use of both the spectral and spatial redundancies of hyperspectral
images, leading to better reconstruction performance. The proposed optimization problem is efficiently solved by using
the alternating direction method of multipliers (ADMM) technique. Experiments on a public database show that our
approach performs better than other state-of-the-art methods on the visual effect and in the quantitative assessment.
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