A multi-frame super-resolution using diffusion registration and a nonlocal variational image restoration

In this paper, we present a new approach of multi-frame super-resolution (SR). The SR techniques strongly depend on the availability of accurate motion estimation. When the estimation of motion is not well established, as usually happens for non-parametric motion, annoying artifacts appear in the super-resolved image. Since SR problems suffer from the motion and blur estimations, new techniques are considered to improve the registration and restoration steps. The proposed method consists of a non-parametric image registration based on diffusion regularization and a nonlocal Laplace regularizer combined with a bilateral filter (BTV) in the reconstruction step to remove noise and motion outliers. The diffusion registration is employed to handle the small deformation between the unregistered images, while the combination of nonlocal Laplace and BTV is used to increase the robustness of the restoration step with respect to the blurring effect and to the noise. We also prove the existence of a solution to the well posed registration problem. Simulation results using different images show the effectiveness and robustness of our algorithm against noise and outliers compared to other existing methods.