Convolutional　neural　networks　have　achieved　good　results　in　single　image　super-resolution.　Nowadays,　image　super-resolution　model　based　on　deep　learning　are　gradually　becoming　lightweight,　and　lightweight　networks　use　grouped　convolution　stacking,　which　hinders　the　channel　Information　flow　between　the　two,　and　weaken　the　feature　representation.　Aiming　at　the　existing　problems　of　the　existing　image　super-resolution　lightweight　model,　this　paper　proposes　a　super-resolution　reconstruction　algorithm　based　on　channel　shuffle.　This　paper　introduces　a　channel　shuffle　mechanism　after　grouped　convolution.　Channel　shuffle　disrupts　the　grouping　order　and　allows　group　convolution　to　obtain　input　data　from　different　groups,　which　allows　the　fusion　of　feature　information　between　different　channels.　At　the　same　time,　this　paper　also　introduces　the　dynamic　activation　function　DY-RELV-B,　which　enhances　the　feature　representation　ability　of　the　network　model　and　further　enhances　the　image　reconstruction　effect.　Experimental　results　show　that　compared　with　LapSRN,　VDSR,　traditional　interpolation　method,　etc.,　the　method　in　this　paper　is　superior　to　other　methods　in　PSNR　on　x2　scale　and　x4　scale.　©　2021　IEEE.