Due　to　the　good　performance,　image　denoising　based　on　Convolutional　Neural　Network　(CNN)　has　been　widely　studied.　However,　most　of　existing　methods　use　a　single　neural　network　for　image　denoising.　The　denoised　images　occur　smooth　edges　(missing　details)　at　a　high　noise　level.　To　address　this　problem,　we　propose　a　dual　convolutional　neural　network　for　image　denoising,　which　is　termed　as　Fusing　Edge-information　in　image　Denoising　based　on　CNN　(FEDnets).　It　consists　of　two　parallel　network　branches,　which　respectively　get　the　denoised　image　and　edge　details　in　an　end-to-end　manner.　In　addition,　the　edges　are　fused　with　the　denoised　image　to　get　a　clearer　and　more　detailed　image.　Experimental　results　show　that　FEDnets　can　be　effectively　applied　to　noise　removal　tasks　and　recover　clearer　images　with　more　edge　details　and　textures　features.　©　2020　IEEE.