Image priors play a key role in low-level vision tasks. Over the years, many priors have been proposed, based on a wide variety of principles. While different priors capture different geometric properties, there is currently no unified approach to interpreting and comparing priors of different nature. This limits our ability to analyze failures or successes of image models in specific settings, and to identify potential improvements. In this paper, we introduce a simple technique for visualizing image priors. Our method determines how images should be deformed so as to best conform to a given image model. The deformed images constructed this way, highlight the elementary geometric structures to which the prior resonates. We use our approach to study various popular image models, and reveal interesting behaviors, which were not noticed in the past. We confirm our findings through denoising experiments. These validate that the structures we reveal as ‘optimal’ for a specific prior are indeed better denoised by this prior.