Super-resolution video restoration with model uncertainties

To solve the problem of super-resolution restoration that algorithm performances are strongly affected by model uncertainties, e.g. inaccurate motion estimations, a new super-resolution algorithm is presented. The video restoration problem is modeled as a time-varying discrete linear dynamical system, and the model uncertainties are considered as perturbations to the system. The algorithm to solve this model is based on a modified Kalman filter, and perturbations are estimated from the motion-compensated prediction residual (MCPR). Experiments for real world video were taken to show the robustness of this algorithm to model uncertainties.