One of the major challenges in multi-view imaging is the definition of a representation that reveals the intrinsic geometry of the visual information. Sparse image representations with overcomplete geometric dictionaries offer a way to efficiently approximate these images, such that the multi-view geometric structure becomes explicit in the representation. However, the choice of a good dictionary in this case is far from obvious. We propose a new method for learning overcomplete dictionaries that are adapted to the joint representation of stereo images. We first formulate a sparse stereo image model where the multi-view correlation is described by local geometric transforms of dictionary elements (atoms) in two stereo views. A maximum-likelihood (ML) method for learning stereo dictionaries is then proposed, where a multi-view geometry constraint is included in the probabilistic model. The ML objective function is optimized using the expectation-maximization algorithm. We apply the learning algorithm to the case of omnidirectional images, where we learn scales of atoms in a parametric dictionary. The resulting dictionaries provide better performance in the joint representation of stereo omnidirectional images as well as improved multi-view feature matching. We finally discuss and demonstrate the benefits of dictionary learning for distributed scene representation and camera pose estimation.