Weighted Super Resolution Reconstruction Based on an Adaptive Regularization Parameter

Recently least square estimator of L₂ norm minimization and L₁ norm minimization estimator are two popular algorithms in super resolution reconstruction. Thus in this paper, the pros and cons of L₁ norm and L₂ norm estimator are first analyzed, then they are weighted and combined, and adopting an approximate total variation regularization method, we proposed a weighted super resolution reconstruction algorithm based on an adaptive regularization parameter. The adaptive method regards the regularization parameter as a function of restored image. Experiments demonstrate that this method not only has better image edge-preserving and efficiently removes the visual artifacts and noise, but also enhances the quality of the restoration images and has better super resolution performance.