Depth Estimation Through a Generative Model of Light Field Synthesis
  Light field photography captures rich structural information that may facilitate a number of traditional image processing and computer vision tasks. A crucial ingredient in such endeavors is accurate depth recovery. We present a novel framework that allows the recovery of a high quality continuous depth map from light field data. To this end we propose a generative model of a light field that is fully parametrized by its corresponding depth map. The model allows for the integration of powerful regularization techniques such as a non-local means prior, facilitating accurate depth map estimation. 
Mehdi S. M. Sajjadi and Rolf K\"ohler and Bernhard Sch\"olkopf and   Michael Hirsch
cs.CV cs.GR