Attention　mechanisms　have　been　introduced　to　exploit　deep-level　information　for　image　restoration　by　capturing　feature　dependencies.　However,　existing　attention　mechanisms　often　have　limited　perceptual　capabilities　and　are　incompatible　with　low-power　devices　due　to　computational　resource　constraints.　Therefore,　we　propose　a　feature　enhanced　cascading　attention　network　(FECAN)　that　introduces　a　novel　feature　enhanced　cascading　attention　(FECA)　mechanism,　consisting　of　enhanced　shuffle　attention　(ESA)　and　multi-scale　large　separable　kernel　attention　(MLSKA).　Specifically,　ESA　enhances　high-frequency　texture　features　in　the　feature　maps,　and　MLSKA　executes　the　further　extraction.　The　rich　and　fine-grained　high-frequency　information　are　extracted　and　fused　from　multiple　perceptual　layers,　thus　improving　super-resolution　(SR)　performance.　To　validate　FECAN＇s　effectiveness,　we　evaluate　it　with　different　complexities　by　stacking　different　numbers　of　high-frequency　enhancement　modules　(HFEM)　that　contain　FECA.　Extensive　experiments　on　benchmark　datasets　demonstrate　that　FECAN　outperforms　state-of-the-art　lightweight　SR　networks　in　terms　of　objective　evaluation　metrics　and　subjective　visual　quality.　Specifically,　at　a　x　4　scale　with　a　121　K　model　size,　compared　to　the　second-ranked　MAN-tiny,　FECAN　achieves　a　0.07　dB　improvement　in　average　peak　signal-to-noise　ratio　(PSNR),　while　reducing　network　parameters　by　approximately　19%　and　FLOPs　by　20%.　This　demonstrates　a　better　trade-off　between　SR　performance　and　model　complexity.