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.
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