High-spatial　hyperspectral　(HH)　image　reconstruction　using　both　high-spatial　multispectral　(HM)　image　and　low-spatial　hyperspectral　(LH)　image　over　the　same　scene　is　widely　used　in　many　real　applications.　Nevertheless,　the　pair　of　HM　image　and　LH　image　over　the　same　scene　is　hard　to　obtain.　To　solve　this　problem,　a　new　HH　image　reconstruction　method　using　spectral　library-based　dictionary　learning　(named　as　HIRSL)　is　proposed　in　this　paper,　only　from　one　HM　image.　The　above　reconstruction　problem　is　formulated　in　the　framework　of　sparse　representation,　as　an　estimation　of　the　band　matching　matrix,　the　spectral　dictionary,　and　the　sparse　coefficients.　More　specifically,　a　band　matching　method　is　proposed　for　mapping　the　common　spectral　library　to　a　specific　spectral　library　corresponding　to　the　reconstructed　HH　image　in　spectral　domain.　Then,　an　efficient　spectral　dictionary　learning　method　is　proposed　for　the　construction　of　spectral　dictionary　using　the　matched　specific　spectral　library,　which　avoids　the　dependence　of　the　LH　image　over　the　same　scene.　Finally,　the　sparse　coefficients　of　the　HM　image　with　respect　to　the　learned　spectral　dictionary　are　estimated　using　the　alternating　direction　method　of　multipliers　without　nonnegative　constraint.　Comparison　results　on　simulated　and　real　data　sets　with　the　relative　state-of-the-art　methods　demonstrate　that　even　only　using　one　HM　image,　our　proposed　method　achieves　a　comparable　reconstruction　quality　of　high-spatial　hyperspectral　image　both　in　spatial　and　spectral　domains.