Removing　undesirable　reflections　from　a　single　image　captured　through　a　glass　surface　is　of　broad　application　to　various　image　processing　and　computer　vision　tasks,　but　it　is　an　ill-posed　and　challenging　problem.　Existing　traditional　single　image　reflection　removal(SIRR)　methods　are　often　less　efficient　to　remove　reflection　due　to　the　limited　description　ability　of　handcrafted　priors.　State-of-the-art　learning　based　methods　often　cause　instability　problems　because　they　are　designed　as　unexplainable　black　boxes.　In　this　paper,　we　present　an　explainable　approach　for　SIRR　named　model-guided　unfolding　network(MoG-SIRR),　which　is　unfolded　from　our　proposed　reflection　removal　model　with　non-local　autoregressive　prior　and　dereflection　prior.　In　order　to　complement　the　transmission　layer　and　the　reflection　layer　in　a　single　image,　we　construct　a　deep　learning　framework　with　two　streams　by　integrating　reflection　removal　and　non-local　regularization　into　trainable　modules.　Extensive　experiments　on　public　benchmark　datasets　demonstrate　that　our　method　achieves　superior　performance　for　single　image　reflection　removal.　©　2021　ACM.