In this paper, we present a new image restoration framework based on two high-level regularizations that can predict and preserve the better informative structures in the image. The sparse representation of a blurred image is first obtained to globally encode the salient structures by applying a group of coupled framelet filters. Then a physical meaning regularizer is derived to estimate the point spread function based on the frequency response characteristics of the image. Moreover, based on the operator of structure tensor, a novel nonlocal total variation as the regularizer is established to measure the image variation and non-local self-similarity. Finally, these two high-level regularizers are integrated into an objective function to constrain the ill-posedness. Compared with the state-of-the-art restoration methods, our algorithm can not only suppress strong noises effectively but also recover the sharp structures from the severe and complex blurred images.