Spectral　or　spatial　dictionary　has　been　widely　used　in　fusing　low-spatial-resolution　hyperspectral　(LH)　images　and　high-spatial-resolution　multispectral　(HM)　images.　However,　only　using　spectral　dictionary　is　insufficient　for　preserving　spatial　information,　and　vice　versa.　To　address　this　problem,　a　new　LH　and　HM　image　fusion　method　termed　OTD　using　optimized　twin　dictionaries　is　proposed　in　this　paper.　The　fusion　problem　of　OTD　is　formulated　analytically　in　the　framework　of　sparse　representation,　as　an　optimization　of　twin　spectral-spatial　dictionaries　and　their　corresponding　sparse　coefficients.　More　specifically,　the　spectral　dictionary　representing　the　generalized　spectrums　and　its　spectral　sparse　coefficients　are　optimized　by　utilizing　the　observed　LH　and　HM　images　in　the　spectral　domain;　and　the　spatial　dictionary　representing　the　spatial　information　and　its　spatial　sparse　coefficients　are　optimized　by　modeling　the　rest　of　high-frequency　information　in　the　spatial　domain.　In　addition,　without　non-negative　constraints,　the　alternating　direction　methods　of　multipliers　(ADMM)　are　employed　to　implement　the　above　optimization　process.　Comparison　results　with　the　related　state-of-the-art　fusion　methods　on　various　datasets　demonstrate　that　our　proposed　OTD　method　achieves　a　better　fusion　performance　in　both　spatial　and　spectral　domains.