Hyperspectral imagery super-resolution by image fusion and compressed sensing

Low spatial resolution is the mainly drawback of hyperspectral imaging. Image super-resolution techniques can be applied to overcome the limits. This paper presents a new framework for improving the spatial resolution of hyperspectral images base by combing high-resolution spectral information and high-resolution spatial information by image fusion and compressed sensing. Based on the compressed sensing theory, small patches of hyperspectral observations from different wavelengths can be represented as weighted linear combinations of a small number of atoms in dictionary which is trained by using panchromatic images. Then hyperspectral image super-resolution is treated as a special image fusion problem with sparse constraints. To make the super-resolution reconstruction more accurate, local manifold projection is used as a regulation term. Extensive experiments on image super-resolution validate that proposed method achieves much better results.