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.