In　order　to　expand　the　adaptive　learning　ability　of　convolutional　neural　network　in　image　super‑　resolution　algorithm　on　multiple　scale　features　and　improve　the　network　performance，this　paper　proposed　an　optimization　structure　of　Transformer　network　based　on　cascade　residual　method　for　image　super‑resolution　reconstruction.　Firstly，the　network　adopted　a　cascaded　residual　structure，which　enhanced　the　iterative　reuse　and　information　sharing　ability　of　low　and　middle　order　features；Secondly，channel　attention　mechanism　was　introduced　into　Transformer　structure　to　enhance　network　feature　expression　and　adaptive　learning　capability　of　channel　weights；Finally，the　sensing　module　in　Transformer　network　structure　was　optimized　as　a　cascade　sensing　module　to　expand　the　network　depth　and　enhance　the　feature　expression　capability　of　the　model.　Reconstruction　tests　of　2x，3x　and　4x　magnification　were　carried　out　on　Set5，Set14，BSD100，Urban100　and　Manga109　data　sets　and　compared　with　mainstream　methods.　Objective　evaluation　results　showed　that　under　Set5　data　set　with　4x　magnification　factor，Compared　with　other　mainstream　methods，the　peak　signal‑to‑noise　ratio　of　the　image　obtained　in　this　paper　is　increased　by　1.　14　dB　on　average，and　the　average　structural　similarity　is　increased　by　0.　019.　Combined　with　the　subjective　evaluation　results，it　is　shown　that　the　proposed　method　has　better　image　reconstruction　effect　than　other　mainstream　methods，and　the　restored　image　texture　details　are　clearer.　©　2024　Chinese　Academy　of　Sciences.　All　rights　reserved.