To　enhance　the　spatial　resolution　of　hyperspectral　image,　a　hyperspectral　image　super-resolution　restoration　algorithm　based　on　redundant　dictionary　was　presented　in　this　paper.　By　training　a　group　of　high　and　low　resolution　redundant　dictionary,　the　corresponding　image　element　curve　of　high　and　low　resolution　was　made　to　have　the　same　sparse　representation　coefficients　in　sparse　decomposition　based　on　redundant　dictionary　in　this　algorithm.　During　the　process　of　super-resolution　restoration,　the　low　resolution　of　hyperspectral　image　sparse　decomposes　based　on　low　resolution　redundant　dictionary.　The　high　resolution　image　was　reconstructed　by　using　the　sparse　representation　coefficients　and　the　high　resolution　dictionary.　The　experimental　results　show　that,　compared　with　the　image　patch　based　sparse　super　resolution　algorithm　and　the　traditional　image　bilinear　interpolation　method,　the　PSNR　of　image　reconstruction　is　significantly　enhanced.　The　algorithm　sparse　decomposes　the　hyperspectral　image　along　the　spectral　dimension　to　avoid　the　traditional　algorithm　problem　of　spectral　distortion　caused　by　restoration.　The　computational　complexity　of　the　algorithm　is　significantly　reduced.　©,　2015,　Beijing　University　of　Technology.　All　right　reserved.