While　various　promising　phasor　measurement　unit　(PMU)　data-driven　applications　have　been　developed　for　modern　smart　grids,　how　to　improve　the　overall　PMU　data　quality　to　ensure　the　reliability　of　these　applications　in　practice　still　remains　an　open　issue.　Considering　the　challenging　task　of　missing　PMU　data　correction　(MPDC)　in　practical　complicated　and　noisy　measurement　contexts,　this　article　develops　a　novel　spatiotemporal　correlation　learning　scheme　(SCLS)　for　online　MPDC　in　smart　grids.　In　particular,　the　SCLS　is　strategically　realized　with　two　successive　modules.　First,　from　four　complementary　spatiotemporal　perspectives,　statistical　missing　data　imputation　is　carried　out　to　derive　initial　correction　results　in　noisy　contexts.　Second,　a　well-designed　deep　learning　architecture　with　the　integration　of　convolutional　neural　network　(CNN)　and　residual　learning　techniques　is　introduced　to　refine　the　correction　results.　With　the　help　of　these　two　modules,　the　SCLS　is　capable　of　performing　precise　MPDC　for　regional　PMU　measurements　as　well　as　filtering　out　potential　noises.　Extensive　numerical　test　results　on　the　IEEE　39-bus　test　system　and　the　real-world　Guangdong　power　grid　in　South　China　demonstrate　the　efficacy　of　the　SCLS　in　practical　complicated　contexts.