The　existing　single-image　super-resolution　convolutional　neural　network　usually　elevates　the　resolution　to　the　specified　scale　in　one　step　in　the　final　image　reconstruction　part.　However,　this　may　lead　to　the　incomplete　reconstruction　of　some　detailed　features.　Therefore,　we　propose　a　super-resolution　reconstruction　algorithm　based　on　progressive　architecture.　For　large-scale　scaling,　high-　quality　images　can　be　gradually　generated　from　coarse　to　fine　in　progressive　reconstruction.　An　adaptive　high-frequency　residual　module　is　designed　to　learn　the　image　features　through　two　branches　of　low　frequency　and　high　frequency,　respectively,　and　the　output　is　adaptive　fusion.　This　process　is　embedded　in　a　Features　cut　Block　to　refine　feature　extraction　during　reconstruction,　capture　the　dependency　between　the　LR-HR　image　pairs,　and　introduce　an　efficient　channel　and　spatial　attention　mechanism　to　fuse　the　image　information.　Experimental　results　show　that　this　algorithm　has　good　performance　and　can　enhance　the　reconstruction　ability　of　images.　©　2023,　John　Wiley　and　Sons　Inc.　All　rights　reserved.