An Optimal Fluid Optical Flow Registration for Super-resolution with Lamé Parameters Learning

The main idea of multi-frame super-resolution (SR) algorithms is to recover a single high-resolution image through a series of low-resolution ones of a captured scene. The success of the SR approaches is often related to well registration and restoration steps. In this work, we propose a new approach based on fluid optical flow image registration and a second-order regularization term to treat both the registration and restoration steps. The fluid registration is introduced to avoid misregistration errors, while the second-order regularization resolved by the Bregman iteration is employed to reduce the image artifacts. Moreover, we propose a bilevel supervised learning framework to compute the Lamé coefficients λ and μ , which perform the nonparametric registration of the super-resolution result. The numerical part demonstrated that the proposed method copes with some competitive SR methods.