Convolutional　neural　networks　are　constrained　in　adaptively　capturing　information　due　to　the　use　of　fixed-size　kernels.　Although　they　provide　a　wide　receptive　field　and　achieve　competitive　performance　with　fewer　parameters　by　using　decomposed　large　kernels,　they　lack　adaptability.　Therefore,　we　propose　the　dynamic　large　kernel　network　(DLKN)　for　lightweight　image　super-resolution.　Specifically,　we　design　a　basic　convolutional　block　of　feature　aggregation　groups,　akin　to　the　transformer　architecture.　It　comprises　a　dynamic　large　kernel　attention　block　and　a　local　feature　enhancement　block　that　can　adaptively　utilize　information.　In　our　dynamic　large　kernel　attention　block,　we　decompose　the　large　kernel　convolution　into　kernels　with　different　sizes　and　expansion　rates.　We　then　fuse　their　information　for　weight　selection,　dynamically　adjusting　the　proportion　of　information　from　different　receptive　fields.　The　local　feature　enhancement　block　significantly　improves　local　feature　extraction　with　low　parameter　counts.　It　encourages　interactions　between　local　spatial　features　by　decomposing　the　convolution　into　horizontally　and　vertically　cascading　kernels.　Experimental　results　on　benchmark　datasets　demonstrate　that　our　proposed　model　achieves　excellent　performance　in　lightweight　and　performance-oriented　super-resolution　tasks.　It　successfully　balances　the　relationship　between　performance　and　model　complexity.　The　code　is　available　at　https://github.com/LyTinGiu/DLKN_SR.　©　The　Author(s),　under　exclusive　licence　to　Springer-Verlag　GmbH　Germany,　part　of　Springer　Nature　2024.