Unmanned　surface　vehicle　(USV)　is　currently　a　hot　research　topic　in　maritime　communication　network　(MCN),　where　denoising　and　semantic　segmentation　of　maritime　images　taken　by　USV　have　been　rarely　studied.　The　former　has　recently　researched　on　autoencoder　model　used　for　image　denoising,　but　the　existed　models　are　too　complicated　to　be　suitable　for　real-time　detection　of　USV.　In　this　paper,　we　proposed　a　lightweight　autoencoder　combined　with　inception　module　for　maritime　image　denoising　in　different　noisy　environments　and　explore　the　effect　of　different　inception　modules　on　the　denoising　performance.　Furthermore,　we　completed　the　semantic　segmentation　task　for　maritime　images　taken　by　USV　utilizing　the　pretrained　U-Net　model　with　tuning,　and　compared　them　with　original　U-Net　model　based　on　different　backbone.　Subsequently.　we　compared　the　semantic　segmentation　of　noised　and　denoised　maritime　images　respectively　to　explore　the　effect　of　image　noise　on　semantic　segmentation　performance.　Case　studies　are　provided　to　prove　the　feasibility　of　our　proposed　denoising　and　segmentation　method.　Finally,　a　simple　integrated　communication　system　combining　image　denoising　and　segmentation　for　USV　is　shown.