In　the　field　of　non-destructive　testing　(NDT),　low-cost　uncooled　infrared　(IR)　sensors　cannot　generate　high-speed　thermography　detection　results　with　low　levels　of　noise　and　high　sensitivity.　Hence,　this　paper　proposes　a　compressive　sensing-based　postprocessing　method　for　the　results　of　uncooled　IR　sensors　to　obtain　high-quality　and　super-resolution　NDT　imaging　results.　Super-resolution　IR　images　are　generated　using　low-resolution　IR　images　and　sparse　dictionaries　generated　from　randomly　sampled　raw　patches　of　training　images.　Super-resolution　IR　images　and　pulsed　phase　thermography　(PPT)　are　combined　to　improve　the　NDT　results　for　carbon　fiber-reinforced　polymer　(CFRP)　specimens　with　artificial　defects　engraved　on　them.　The　results　of　the　PPT　experiments　show　that　the　compressive　sensing-based　super-resolution　algorithm　can　be　used　to　double　the　resolution　of　PPT　phase　images.　The　reconstructed　PPT　phase　images　further　show　that　the　proposed　method　can　produce　sharp　defect　edges　while　preserving　the　original　texture,　which　will　enable　the　use　of　uncooled　IR　sensors　in　a　wider　range　of　NDT　applications.