Single　Image　Super　Resolution　(SISR)　refers　to　the　reconstruction　of　high　resolution　images　from　a　low　resolution　image.　Traditional　neural　network　methods　typically　perform　super-resolution　reconstruction　in　the　spatial　domain　of　an　image,　but　these　methods　often　ignore　important　details　in　the　reconstruction　process.　In　view　of　the　fact　that　wavelet　transform　can　separate　the　＇rough＇　and　＇detail＇　features　of　image　content,　this　study　proposes　a　wavelet-based　deep　residual　network　(DRWSR).　Different　from　other　traditional　convolutional　neural　networks,　the　high-resolution　image　(HR)　is　directly　derived.　This　method　uses　a　multi-stage　learning　strategy　to　first　infer　the　wavelet　coefficients　corresponding　to　the　high-resolution　image　and　then　reconstruct　the　super-resolution　image　(SR).　In　order　to　obtain　more　information,　the　method　uses　a　flexible　and　scalable　deep　neural　network　with　residual　nested　residuals.　In　addition,　the　proposed　neural　network　model　is　optimized　by　combining　the　loss　function　of　image　space　and　wavelet　domain.　The　proposed　method　is　carried　out　on　Set5,　Set14,　BSD100,　Urban100,　and　other　datasets.　The　experimental　results　show　that　the　proposed　visual　effect　and　peak　signal-to-noise　ratio　(PNSR)　are　better　than　the　related　image　super-resolution　method.　©　Copyright　2019,　Institute　of　Software,　the　Chinese　Academy　of　Sciences.　All　rights　reserved.