In many image blur removal schemes, a proper point spread function is usually estimated in advance from the blurry image, then the latent image comes out by using existing non-blind techniques. However, some of the techniques suffer from strong artifacts. Therefore, an efficient non-blind method plays an important role in image restoration issues. In most models, image priors act as the regularization terms that hold image details and suppress noises. This paper introduces a new image prior based on a parameterized scaled Gaussian model and a gamma distribution, with hyperparameters based on the statistical properties of tens of thousands of images. Our regularized cost function is then formed via a Bayesian hierarchical approach. It consists of a data fidelity term and a series of constraints on image gradients in multiple orientations. The former is used to assure the best approximation of the original image, and the latter is for preserving sharp edges. The optimization problem is solved by an effective tail-recursive algorithm based on the conjugate descent technique. Experimental results show that our model can both deal with simulated data and real scenes. The comparisons show our method outperforms others and achieves promising results.