Super-Resolution restoration is a technique for estimating an unaliased high-resolution image (or a sequence) from an aliased video sequence and combating additive noise and blurring due to the finite detector size and optics. In this paper, an improved Bayesian MAP estimator with multi-scale edge-preserving regularization for super-resolution restoration is proposed. The confidence of the motion estimation result is validated to eliminate motion artifact. The wavelet representation of an image is utilized to define the spatial activity measure of the image, and further to construct a novel multi-scale Huber-Markov model. The experimental results show that the multi-scale Huber-Markov model can be incorporated into Bayesian MAP estimator to preserve the edges of the super-resolution image effectively. This proposed algorithm is widely used for remote sensing, medical imaging, high-definition television (HDTV) standard and creation of synthetic ‘video zoom’.