Reliable light field multiwindow disparity estimation
Julia Navarro, Antoni Ramos Buades · 2016
In this work 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. This means that disparity is the same for any pair of consecutive views, in both vertical and horizontal directions. We exploit this fact by computing disparity maps between specific pairs of views. This computation is done by a state of the art two-view stereo method which also provides a mask of incorrect matches. Then, disparity maps and masks are used to obtain a unique and robust estimation. Finally, we show the performance of the algorithm by means of different experiments.