Restoring　Poissonian　images　is　particularly　considered　due　to　astronomical　and　medical　applications　in　recent　years.　In　this　paper,　we　propose　a　novel　noise　removal　method　for　restoration　of　hyperspectral　images　corrupted　by　Poisson　noise,　based　on　spectral　unmixing　technique.　We　formulate　Poissonian　hyperspectral　image　problem　as　an　optimization　problem　where　the　cost　function　consists　of　three　terms.　The　likelihood　of　the　observation　with　Poisson　distribution　is　used　as　the　data-fidelity　term.　The　total　variation　of　the　abundance　for　piecewise　smooth　information　and　the　l1-norm　of　the　abundance　for　sparse　information　are　introduced　as　two　regularization　terms.　Finally,　the　optimization　problem　is　effectively　solved　by　alternating　direction　optimization　algorithm.　Therefore,　the　hyperspectral　image　can　be　well　restored　after　Poisson　noise　is　successfully　removed.　The　experiment　results　show　that　the　proposed　method　has　a　better　performance　than　current　Poissonion　hyperspectral　image　denoising　methods,　in　terms　of　both　image　quality　and　computation　time.　©　2017　Elsevier　B.V.