Due　to　the　absorption　and　scattering　of　underwater　light,　the　underwater　image　suffers　from　distortion　and　loss　of　details,　which　seriously　affects　the　detection　and　recognition　of　subsequent　underwater　target.　In　this　paper,　a　lightweight　fully　convolutional　layer　generative　adversarial　neural　network　DUnet-GAN　is　proposed　to　enhance　underwater　image.　According　to　the　characteristics　of　underwater　image,　this　paper　proposes　a　multi-task　objective　function,　which　enables　the　model　to　enhance　the　image　quality　by　perceiving　the　overall　content,　color,　local　texture　and　style　information　of　the　image.　In　addition,　we　compare　DUnet-GAN　with　some　important　existing　models　and　make　a　quantitative　evaluation.　The　results　show　that　in　EUVP　dataset,　the　PSNR　of　the　proposed　model　is　above　26　dB,　the　SSIM　is　0.　8,　and　the　number　of　parameters　is　11　MB,　which　is　only　5%　of　the　number　of　parameters　of　other　models　with　the　same　performance　and　better　than　the　FunIE-GAN　with　26　MB　parameters.　Meanwhile,　UIQM　is　2.　85,　second　only　to　Cycle-GA　N　model,　and　the　enhancement　effect　is　significant　subjectively.　More　importantly,　the　enhanced　image　provides　better　performance　for　underwater　target　detection　and　other　models,　and　also　meets　the　lightweight　requirements　of　models　for　equipment　such　as　underwater　robots.