Training　image　pairs　are　unavailable　generally　in　real-　world　super-resolution.　Although　the　LR　images　can　be　down-scaled　from　HR　images,　some　real-world　characteristics　(such　as　artifacts　or　sensor　noise)　have　been　removed　from　the　degraded　images.　Therefore,　most　of　state-of-the-　art　super-resolved　methods　often　fail　in　real-world　scenes.In　order　to　address　aforementioned　problem,　we　proposed　an　unsupervised　super-resolved　solution.　The　method　can　be　divided　into　two　stages:　domain　transformation　and　super-resolution.　A　color-guided　domain　mapping　network　was　proposed　to　alleviate　the　color　shift　in　domain　transformation　process.　In　particular,　we　proposed　the　Color　Attention　Residual　Block　(CARB)　as　the　basic　unit　of　the　domain　mapping　network.　The　CARB　which　can　dynamically　regulate　the　parameters　is　driven　by　input　data.　Therefore,　the　domain　mapping　network　can　result　in　the　powerful　generalization　performance.　Moreover,　we　modified　the　discriminator　of　the　super-resolution　stage　so　that　the　network　not　only　keeps　the　high　frequency　features,　but　also　maintains　the　low　frequency　features.　Finally,　we　constructed　an　EdgeLoss　to　improve　the　texture　details.　Experimental　results　show　that　our　solution　can　achieve　a　competitive　performance　on　NTIRE　2020　real-world　super-resolution　challenge.　©　2020　IEEE.