This　paper　proposes　a　new　image　super-resolution　method　based　on　Generative　Adversarial　Network　(GAN).　Firstly,　the　algorithm　model　includes　generating　model　and　discriminant　model,　generating　model　to　generate　high-resolution　image,　discriminating　model　to　distinguish　the　image　true　or　false,　the　original　image　is　true,　and　the　generated　image　is　false.　Using　alternate　training　method,　the　generated　model　and　discriminant　model　achieve　Nash　equilibrium,　and　finally　generate　high-quality　image.　Compared　with　previous　super-resolution　method　based　on　generative　adversarial　network　(SRGAN),　the　following　changes　have　been　made:　modifying　the　network　structure,　removing　the　unnecessary　batch　normalization　layer　in　the　standard　residual　block,　deepening　the　network　layer　number　and　improving　the　loss　function.　The　experimental　results　show　that　compared　with　the　traditional　bicubic　interpolation　method　and　compared　with　SRGAN,　the　proposed　algorithm　improves　the　actual　image　effect,　peak　signal-to-noise　ratio　(PSNR)　and　structural　similarity　index　(SSIM)　in　varying　degrees.　©　2019　IOP　Publishing　Ltd.　All　rights　reserved.