Single　Image　Reflection　Removal　(SIRR)　is　an　active　topic　in　low-level　vision,　aiming　to　eliminate　the　influence　of　reflected　objects　or　light　sources　on　image　quality.　However,　due　to　the　ill-posed　property　of　SIRR　and　the　lack　of　large-scale　real　world　reflection　image　datasets,　existing　methods　degrade　on　real　datasets　and　suffer　from　the　problem　of　reflection　residue.　To　address　these　issues,　we　propose　an　effective　SIRR　network　called　PA-NAFNet.　It　utilizes　a　non-linear　activation-free　network　(NAFNet)　as　the　baseline　and　incorporates　a　pyramid　attention　module　to　capture　long-range　pixel　interactions.　Additionally,　during　the　training　phase,　color　jittering　technique　is　introduced　to　increase　the　diversity　of　the　training　dataset,　thereby　alleviating　potential　color　distortion　issues　after　reflection　removal.　Experimental　results　on　multiple　reflection　removal　benchmark　tests　demonstrate　the　effectiveness　of　PA-NAFNet.　The　relevant　code　is　available　on　this　link.　©　2025　Elsevier　Inc.