Training　overcomplete　dictionary　pair　is　a　critical　step　of　the　mainstream　superresolution　methods.　For　the　high　time　complexity　and　susceptible　to　corruption　characteristics　of　training　dictionary,　an　improved　method　based　on　lifting　wavelet　transform　and　robust　principal　component　analysis　is　reported.　The　high-frequency　components　of　example　images　are　estimated　through　wavelet　coefficients　of　3-tier　lifting　wavelet　transform　decomposition.　Sparse　coefficients　are　similar　in　multiframe　images.　Accordingly,　the　inexact　augmented　Lagrange　multiplier　method　is　employed　to　achieve　robust　principal　component　analysis　in　the　process　of　imposing　global　constraints.　Experiments　reveal　that　the　new　algorithm　not　only　reduces　the　time　complexity　preserving　the　clarity　but　also　improves　the　robustness　for　the　corrupted　example　images.