Hyperspectral image super-resolution based on non-factorization sparse representation and dictionary learning

Non-negative Matrix Factorization is the most typical model for hyperspectral image super-resolution. However, the non-negative restriction on the coefficients limited the efficiency of dictionary expression. Facing this problem, a new hyperspectral image super-resolution method based on non-factorization sparse representation and dictionary learning (called NFSRDL) is proposed in this paper. Firstly, an efficient spectral dictionary learning method is specifically adopted for the construction of spectral dictionary using some low spatial resolution hyperspectral images in the same or similar areas. Then, the sparse codes of the high-resolution multi-bands image with respect to the learned spectral dictionary are estimated using the alternating direction method of multipliers (ADMM) without non-negative constrains. Experimental results on different datasets demonstrate that, compared with the related state-of-the-art methods, our method can improve PSNR over 1.3282 and SAM over 0.0476 in the same scene, and PSNR over 3.1207 and SAM over 0.4344 in the similar scenes.