Generative models can be used, as an alternative to conventional probability densities, to capture the statistical behavior of complicated datasets. Unlike probability densities with which the generation of realizations may become a challenging task, generative models have an inherent ability to easily produce realizations, which, in the case of natural images can be extremely realistic. In many image restoration problems, such as deblurring, colorization, inpainting, super-resolution, etc., probability densities are used as priors, one may therefore wonder whether we can, instead, adopt generative models. Indeed such methods have appeared in the literature, but they require exact knowledge of the transformations responsible for the data distortion and involve regularizer terms with weights that require adjustment. Our approach, by combining maximum a-posteriori probability with maximum likelihood estimation, can successfully restore images in both blind and non-blind modes without the need to fine-tune any regularization parameters. Simulations on deblurring, colorization, and image separation problems with exact knowledge of the transformation demonstrate improved image quality, reduced computational cost compared to existing methods. Comparable results are also enjoyed when the distortion models contain unknown parameters.