Recently,　deep　learning　has　been　successfully　applied　to　the　single-image　super-resolution　(SISR)　with　remarkable　performance.　However,　most　existing　methods　focus　on　building　a　more　complex　network　with　a　large　number　of　layers,　which　can　entail　heavy　computational　costs　and　memory　storage.　To　address　this　problem,　we　present　a　lightweight　Self-Calibrated　Efficient　Transformer　(SCET)　network　to　solve　this　problem.　The　architecture　of　SCET　mainly　consists　of　the　self-calibrated　module　and　efficient　transformer　block,　where　the　self-calibrated　module　adopts　the　pixel　attention　mechanism　to　extract　image　features　effectively.　To　further　exploit　the　contextual　information　from　features,　we　employ　an　efficient　transformer　to　help　the　network　obtain　similar　features　over　long　distances　and　thus　recover　sufficient　texture　details.　We　provide　comprehensive　results　on　different　settings　of　the　overall　network.　Our　proposed　method　achieves　more　remarkable　performance　than　baseline　methods.