This　letter　proposes　a　novel　double　regularization　unmixing-based　method　for　hyperspectral　image　(HSI)　superresolution.　The　proposed　cost　function　contains　two　data-fidelity　terms,　the　endmember　regularization　term　and　the　abundance　regularization　term.　Since　the　double　regularization　unmixing　terms　are　able　to　exploit　the　spatial　structure　information　of　endmember　and　abundance,　respectively,　the　nonnegative　factorization　(spectral　unmixing)　error　is　minimized.　As　a　result,　the　performance　of　the　proposed　HSI　superresolution　method　can　be　enhanced　in　terms　of　noise　suppression　and　the　special　structure　information　preservation　of　reconstruction　images.　Finally,　the　associated　optimization　problem　is　effectively　solved　by　an　alternating　direction　optimization　algorithm.　Simulation　results　illustrate　that　the　proposed　method　has　a　better　performance　than　the　state-of-the-art　methods　in　terms　of　both　visual　effectiveness　and　quality　indices.