An　efficient　corrupted　image　(missing　data)　registration　algorithm　was　proposed.　Firstly,　the　lost　elements　in　the　corrupted　image　was　recovered　by　using　matrix　completion,　then　PCA　(Principal　Component　Analysis)　was　applied　in　SIFT　(Scale　Invariant　Feature　Transform).　To　solve　the　shortcomings　of　128　element　eigenvectors＇　large　storage　and　more　time　to　match,　PCA　was　used　to　decrease　the　element　of　eigenvectors　presenting　descriptors.　Finally,　Gaussian　weighted　Euclidean　distance　instead　of　normal　Euclidean　distance　was　used　to　judge　the　similarity　of　feature　points.　The　experimental　results　show　that　this　algorithm,　when　applied　in　the　computer　vision　field　of　content-based　image　and　video　retrieval,　has　good　stability　and　accuracy　to　corrupted　image　registration.