Periodic Temporal Super Resolution Based on Phase Registration and Manifold Reconstruction

This paper describes a method for periodic temporal super resolution, namely, reconstructing a one period image sequence with high frame-rate from a single quasi-periodic image sequence with low frame-rate. First, the periodic image sequence to be reconstructed is expressed as a manifold in the parametric eigenspace of the phase, namely, period-normalized time. Given an input image sequence, phase registration data in sub-frame order among multiple periods of the image sequence is estimated. The phase registration and manifold reconstruction are alternately executed iteratively within an energy minimization framework that considers data fitness and the smoothness of both the manifold and the phase evolution. The energy minimization problem is solved through three-step coarse-to-fine procedures to avoid local minima. The proposed periodic temporal super resolution is evaluated through the experiments using both simulated and real data in terms of phase noise, the number of input frames, frame-rate, spatial registration noise, and image noise, respectively.