The　reconstruction　of　Poissonian　image　is　an　active　research　area　in　recent　years.　This　paper　proposes　a　novel　method　for　Poissonian　hyperspectral　image　superresolution　by　fusing　a　low-spatial-resolution　hyperspectral　image　and　a　low-spectral-resolution　multispectral　image.　The　fusion　scheme　is　designed　as　an　optimization　problem,　whose　cost　function　consists　of　the　two　data-fidelity　terms　about　Poisson　distribution,　the　sparse　representation　term,　and　the　nonlocal　regularization　term.　The　two　data-fidelity　terms　can　capture　statistical　information　of　Poisson　noise.　The　sparse　representation　term　is　used　for　enhancing　the　quality　of　sparsity-based　signal　reconstruction,　and　the　nonlocal　regularization　term　exploits　the　spatial　similarity　of　hyperspectral　image.　As　a　result,　the　hyperspectral　image　and　multispectral　image　are　well　fused.　Finally,　the　designed　optimization　problem　is　effectively　solved　by　an　alternating　direction　optimization　algorithm.　Simulation　results　illustrate　that　the　proposed　method　has　a　better　performance　than　several　well-known　methods　both　in　terms　of　quality　indexes　and　reconstruction　visual　effect.