In　recent　years,　the　denoising　models　based　on　convolutional　neural　network　(CNN)　have　made　great　progress.　However,　CNN　based　image　denoising　models　lend　to　generate　artifacts　and　blurry　edges.　To　deal　with　this　problem,　this　paper　proposes　a　multi-residuals　network　with　cascade　strategy　to　keep　image　textures,　and　integrates　face　region　constraints　to　loss　function　of　model　optimization.　The　weighted　loss　function　characterizes　the　location　and　gray　probabilities　of　different　face　regions,　which　brings　benefits　to　recover　face-image　sharpness　and　naturalness.　Experimental　results　on　the　Helen　and　IMM　face　datasets　show　that　the　proposed　model　can　suppress　artifacts　in　smooth　regions　and　recover　sharper　edges.