Single　Image　Reflection　Removal　(SIRR)　is　a　hot　topic　in　downstream　tasks　of　computer　vision,　with　the　aim　of　eliminating　undesirable　reflections　in　degraded　images　taken　through　glass.　However,　due　to　the　ill-posed　property　of　SIRR　and　the　lack　of　large-scale　real　world　mixture　images　contaminated　by　reflections,　existing　methods　degrade　on　real　datasets　and　suffer　from　the　problem　of　reflection　residue.　In　this　work,　we　propose　an　efficient　SIRR　pyramid　model　that　can　robustly　recover　real　images　degraded　by　reflections.　Specifically,　our　network　uses　a　Nonlinear　Activation　Free　Network　(NAFNet)　as　a　baseline,　a　variant　of　U-Net,　which　is　capable　of　extracting　feature　maps　of　different　scales.　Furthermore,　to　learn　the　pixel-level　long-distance　feature　correspondence　between　multi-scale　features,　a　pyramid　fusion　module　based　on　scale-agnostic　attention　has　been　embedded　into　the　baseline.　Comprehensive　experimental　results　demonstrate　the　effectiveness　of　our　model.　©　2023　IEEE.