Objective:　Medical　image　analysis　is　particularly　important　for　doctors　to　differential　diagnosis　of　diseases.　Due　to　the　outbreak　of　COVID-19,　how　to　diagnose　COVID-19　accurately　has　become　a　key　issue.　High-resolution　lung　CT　images　can　provide　more　diagnostic　information,　so　there　is　an　urgent　need　to　develop　a　super-resolution　method　to　improve　the　resolution　of　medical　images.　Methods:　In　this　paper,　a　method　based　on　double　paths　with　residual　information　distillation　for　medical　images　super　resolution　(DRIDSR)　is　established.　In　the　low-frequency　path,　shallow　convolutional　network　is　used　to　get　low-frequency　features,　while　in　the　high-frequency　path,　a　residual　information　distillation　module　(RIDM)　is　designed　to　obtain　clearer　high-frequency　features.　RIDM　cascades　multiple　residual　blocks,　and　uses　the　output　of　each　residual　block　as　the　input　of　IDB　for　further　information　distillation.　Finally,　it　merges　the　information　left　by　multiple　IDBs　as　output.　Results:　The　proposed　method　is　tested　on　the　public　dataset　COVID-CT.　The　DRIDSR　reconstruction　quality　of　the　algorithm　is　higher　than　that　of　the　SRCNN,　ESPCN,　VDSR,　IMDN　and　PAN　method　(+2.21　dB,　+2.41　dB,　+1.42　dB,　+0.43　dB,　+0.54　dB　improvement,　respectively)　at　x　3　upscale　factor　and　(+2.35　dB,　+2.17　dB,　+1.59　dB,　+0.48　dB,　+0.56　dB　increase,　respectively)　at　x4　upscale　factor.　While　the　number　of　parameters　and　analysis　time　of　our　model　are　reduced.　Conclusions:　It　is　demonstrated　that　DRIDSR　network　can　obtain　better　performance　and　better　HR　medical　images　than　several　state-of-the-art　SR　methods　in　terms　of　objective　indicators　and　subjective　evaluation.