This　paper　develops　a　Bayesian　dictionary　learning　method　for　hyperspectral　image　super　resolution　in　the　presence　of　mixed　Poisson–Gaussian　noise.　A　likelihood　function　is　first　designed　to　deal　with　the　mixed　Poisson–Gaussian　noise.　A　fusion　optimization　model　is　then　introduced,　including　the　data-fidelity　term　capturing　the　statistics　of　mixed　Poisson–Gaussian　noise,　and　a　beta　process　analysis-based　sparse　representation　regularization　term.　In　order　to　implement　the　proposed　method,　we　use　alternating　direction　method　of　multipliers　(ADMM)　for　simultaneous　Bayesian　nonparametric　dictionary　learning　and　image　estimation.　Compared　with　conventional　dictionary　learning　methods,　the　introduced　dictionary　learning　method　is　based　on　a　popular　beta　process　factor　analysis　(BPFA)　for　an　adaptive　learning　performance.　Simulation　results　illustrate　that　the　proposed　method　has　a　better　performance　than　several　well-known　methods　in　terms　of　quality　indices　and　reconstruction　visual　effects.　©　2017　Elsevier　B.V.