The　significant　improvements　in　image　super-resolution　(SR)　in　recent　years　is　majorly　resulted　from　the　use　of　deeper　and　deeper　convolutional　neural　networks　(CNN).　However,　both　computational　time　and　memory　consumption　simultaneously　increase　with　the　utilization　of　very　deep　CNN　models,　posing　challenges　to　deploy　SR　models　in　realtime　on　computationally　limited　devices.　In　this　work,　we　propose　a　novel　strategy　that　uses　a　teacher-student　network　to　improve　the　image　SR　performance.　The　training　of　a　small　but　efficient　student　network　is　guided　by　a　deep　and　powerful　teacher　network.　We　have　evaluated　the　performance　using　different　ways　of　knowledge　distillation.　Through　the　validations　on　four　datasets,　the　proposed　method　significantly　improves　the　SR　performance　of　a　student　network　without　changing　its　structure.　This　means　that　the　computational　time　and　the　memory　consumption　do　not　increase　during　the　testing　stage　while　the　SR　performance　is　significantly　improved.