To　compensate　for　the　inability　of　inexpensive　uncooled　infrared　(IR)　sensors　to　perform　high-speed　thermography　detection　with　low　noise　and　high　sensitivity　in　the　field　of　non-destructive　testing　(NDT),　a　post-processing　method　for　uncooled　IR　sensors　based　on　compressive　sensing　is　proposed　in　this　paper　to　perform　super-resolution　NDT　thermography.　Super-resolution　IR　phase　images　can　be　generated　by　first　using　a　sparse　representation　of　the　low-resolution　(LR)　IR　images　and　a　sparse　dictionary　generated　from　randomly　sampled　high-resolution　(HR)　raw　training　image　patches　to　generate　an　HR　IR　image　sequence,　and,　subsequently,　implementing　IR　thermography　NDT.　To　verify　the　reliability　of　the　proposed　method,　super-resolution　IR　images　were　combined　with　pulsed　phase　thermography　(PPT)　to　verify　bonded　joint　structures　in　carbon　fibre-reinforced　polymer　(CFRP).　Compared　to　other　methods,　the　proposed　method　reduces　the　mean　square　error　(MSE)　by　11.13%　and　yields　a　better　visual　effect　on　the　PPT　phase　image　reconstructed　using　three-fold　super-resolution.　The　proposed　method　not　only　produces　sharp　defect　edges,　but　also　retains　the　original　texture,　which　is　expected　to　enable　wider　application　of　uncooled　IR　sensors　in　NDT　applications　in　the　future.