Low level vision via switchable Markov random fields

Markov random fields play a central role in solving a variety of low level vision problems, including denoising, in-painting, segmentation, and motion estimation. Much previous work was based on MRFs with hand-crafted networks, yet the underlying graphical structure is rarely explored. In this paper, we show that if appropriately estimated, the MRF´s graphical structure, which captures significant information about appearance and motion, can provide crucial guidance to low level vision tasks. Motivated by this observation, we propose a principled framework to solve low level vision tasks via an exponential family of MRFs with variable structures, which we call Switchable MRFs. The approach explicitly seeks a structure that optimally adapts to the image or video along the pursuit of task-specific goals. Through theoretical analysis and experimental study, we demonstrate that the proposed method addresses a number of drawbacks suffered by previous methods, including failure to capture heavy-tail statistics, computational difficulties, and lack of generality.