In this paper, we propose a novel method for depth estimation in light fields which employs a specifically designed sparse decomposition to leverage the depth-orientation relationship on its epipolar plane images. The proposed method learns the structure of the central view and uses this information to construct a light field dictionary for which groups of atoms correspond to unique disparities. This dictionary is then used to code a sparse representation of the light field. Analysing the coefficients of this representation with respect to the disparities of their corresponding atoms yields an accurate and robust estimate of depth. In addition, if the light field has multiple depth layers, such as for reflective or transparent surfaces, statistical analysis of the coefficients can be employed to infer the respective depth of the superimposed layers.