While most existing enhancement tools for photographs have universal auto-enhancement functionality, recent research shows that users can have personalized preferences. In this paper, we explore whether such personalized preferences in image enhancement tend to cluster and whether users can be grouped according to such preferences. To this end, we analyze a comprehensive data set of image enhancements collected from 336 users via Amazon Mechanical Turk. We find that such clusters do exist and can be used to derive methods to learn statistical preference models from a group of users. We also present a probabilistic framework that exploits the ideas behind collaborative filtering to automatically enhance novel images for new users. Experiments show that inferring clusters in image enhancement preferences results in better prediction of image enhancement preferences and outperforms generic auto-correction tools.