Image　denoising　is　a　hot　topic　in　many　research　fields,　such　as　image　processing　and　computer　vision.　With　the　development　of　deep　learning,　deep　neural　networks　are　widely　used　for　image　denoising　and　have　achieved　good　effectiveness.　Inspired　by　the　characteristics　of　feed-forward　denoising　convolutional　neural　network　(DnCNN)　and　biological　neuron　response,　we　propose　a　Symmetry-Rectifier　Linear　Unit　(SyReLU)　and　further　offer　a　corresponding　SyReLU　activation　function,　which　has　a　better　consistency　with　biological　neuron　characteristics　in　comparison　with　other　activation　functions,　e.g.　Rectifier　Linear　Unit　(ReLU)　and　Leaky　Rectifier　Linear　Unit(LReLU).　Also,　in　order　to　denoise　image,　we　use　SyReLU　activation　function　for　residual　learning　of　CNN　(e.g.　DnCNN).　Specially,　the　experimental　results　indicate　DnCNN　with　SyReLU　can　achieve　better　effectiveness　than　DnCNN　with　other　activation　functions　(e.g.ReLU　and　LReLU)　for　image　denosing　on　Set12　and　BSD68　datasets.　Briefly,　the　proposed　method　plays　an　important　role　in　the　development　of　activation　function　and　is　very　useful　in　deep　neural　networks　for　image　denosing.