The aim of super-resolution (SR) algorithms is to recover high-resolution (HR) images and videos from low-resolution (LR) ones. Since the SR is considered as an ill-posed minimization problem, regularization techniques are then considered. The choice of the regularization term plays a major role in the quality of the obtained HR image. Even if many terms have been proposed in the literature, they still suffer from different undesirable artifacts. To address these weaknesses, we propose a variational SR model based on Huber-Norm using Bregman distances. This offers the new model to be more consistent against contrast loss and smoothing gray values, in contrast, strong edges and contours are well preserved in the reconstruct HR image. Moreover, the use of first-order primaldual algorithm with an adaptive regularization parameter choice assure the convergence to the desired HR image, in a fast way, preserving important image features. As a result, the proposed algorithm shows promising results for various real and synthetic datasets compared with other methods. We treat the multi-frame super-resolution task based on Bregman distance.We propose a variational SR model based on Huber-Norm and bilateral total variation.The improved regularization is efficient in degraded image super-resolution task using primaldual algorithm.The proposed method gives better performance comparing with other approaches.