Image　denoising　is　to　estimate　a　latent　clean　image　from　the　noisy　image.　Existing　denoising　algorithms　generally　neglect　smooth　edges　(missing　details)　while　removing　noises.　In　order　to　solve　this　problem,　we　propose　an　image　denoising　algorithm　called　fusion　canny-edge　operator　image　denoising　based　on　CNN　(FCDnet),　which　is　composed　of　a　denoising　module　based　on　Convolutional　neural　network　(CNN),　a　canny　edge　module　based　on　canny　operator　and　a　fusion　module　based　on　residual　block.　In　addition,　the　edge　extracted　by　canny　edge　extraction　module　is　fused　with　the　denoised　image　extracted　by　the　denoising　module　to　get　a　clearer　and　more　detailed　image.　Experimental　results　show　that　the　proposed　algorithm　obtains　higher　PSNR　with　more　edge　details　and　textures　features　than　state-of-the-art　methods　on　multiple　datasets,　i.e.,　Set5,　Set14　and　McMaster.　©　2020　ACM.