I maging synthesis methods open a new door to help scientists for further study on porous materials. H igh resolution images are required t o analyze the macroscopic propert ies of porous media. However, a few degenerated high resolution samples are available because of constraints, and low resolution measurements (such as MRI images) cannot fully describe the medium. Computer-aided approaches can help the science of porous media by generating many artificial high resolution samples using the information of available data. In this paper, a novel discriminative graphical framework is proposed which statistically models the synthesis problem. The probability distribution of high resolution image of a porous medium given a low resolution measurement is modeled by conditional random fields (CRF). A Monte Carlo approach is proposed to sample the constructed model and to generate high resolution samples. Moreover, a hierarchical CRF is proposed for gradual synthesis of high resolution porous media images. The success of the models is shown and compared through several experimental results.