The　use　of　low-dose　computed　tomography　(LDCT)　in　medical　practice　can　effectively　reduce　the　radiation　risk　of　patients,　but　it　may　increase　noise　and　artefacts,　which　can　compromise　diagnostic　information.　The　methods　based　on　deep　learning　can　effectively　improve　image　quality,　but　most　of　them　use　a　training　set　of　aligned　image　pairs,　which　are　difficult　to　obtain　in　practice.　In　order　to　solve　this　problem,　on　the　basis　of　the　Wasserstein　generative　adversarial　network　(GAN)　framework,　we　propose　a　generative　adversarial　network　combining　multi-perceptual　loss　and　fidelity　loss.　Multi-perceptual　loss　uses　the　high-level　semantic　features　of　the　image　to　achieve　the　purpose　of　noise　suppression　by　minimizing　the　difference　between　the　LDCT　image　and　the　normal-dose　computed　tomography　(NDCT)　image　in　the　feature　space.　In　addition,　L2　loss　is　used　to　calculate　the　loss　between　the　generated　image　and　the　original　image　to　constrain　the　difference　between　the　denoised　image　and　the　original　image,　so　as　to　ensure　that　the　image　generated　by　the　network　using　the　unpaired　images　is　not　distorted.　Experiments　show　that　the　proposed　method　performs　comparably　to　the　current　deep　learning　methods　which　utilize　paired　image　for　image　denoising.