Robust and dense depth estimation for light field images

We propose a depth estimation method for light field images. Light field images can be considered as a collection of 2D images taken from different viewpoints arranged in a regular grid. We exploit this configuration and compute the disparity maps between specific pairs of views. This computation is carried out by a state of the art two-view stereo method providing a non dense disparity estimation. We propose a disparity interpolation method increasing the density and improving the accuracy of this initial estimate. Disparities obtained from several pairs of views are fused to obtain a unique and robust estimation. Finally, different experiments on synthetic and real images show how the proposed method outperforms state of the art results.