Transformer　has　become　one　of　the　main　architectures　in　deep　learning,　showing　impressive　performance　in　various　vision　tasks,　especially　for　image　super-resolution　(SR).　However,　due　to　the　usage　of　high　-resolution　input　images,　most　current　Transformer-based　image　super-resolution　models　have　a　large　number　of　parameters　and　high　computational　complexity.　Moreover,　some　components　employed　in　the　Transformer　may　be　redundant　for　SR　tasks,　which　may　limit　the　SR　performance.　In　this　work,　we　propose　an　efficient　and　concise　model　for　image　super-resolution　tasks　termed　Residual　Separation　Hybrid　Attention　Network　(RSHAN),　which　aims　to　solve　the　problems　of　redundant　components　and　insufficient　ability　to　extract　high-frequency　information　of　Transformer.　Specifically,　we　present　the　Residual　Separation　Hybrid　Attention　Module　(RSHAM)　which　fuses　the　local　features　extracted　by　the　convolutional　neural　network　(CNN)　branch　and　the　long-range　dependencies　extracted　by　Transformers　to　improve　the　performance　of　RSHAN.　Extensive　experiments　on　numerous　benchmark　datasets　show　that　the　proposed　method　outperforms　state-of-the-art　SR　methods　by　up　to　0.11　dB　in　peak　signal-to-noise　ratio　(PSNR)　metric,　while　the　computational　complexity　and　the　inference　time　is　reduced　by　5%　and　10%,　respectively.