Light field images are represented by capturing a densely projected rays from object to camera sensor. In this paper we propose a novel method for depth estimation from light field epipolar plane image (EPI). Contrary to the conventionally used depth estimation method such as stereo matching, the depth map is generated without high computational complexity via EPI. In order to obtain an accurate depth value from EPI, optimal angular value has to be founded for each pixels. If we consider all the angular candidate value for optimal angle value selection, that cause high computational complexity. Instead we consider all candidate value, reduce the angular candidate by analyzing the EPI patterns. In addition, to improve the quality of estimated depth map from EPI, occlusion area is handled before computing a matching cost. As a pre-processing, average and variance value are computed within specific window size to detect and replace the occlusion area. To validate the efficiency of our algorithm, we experiment with computer graphics and also dense light field data set. The experiment results show that our algorithm achieve better performance than conventionally used depth estimation methods.
Ji-Hun Mun, Yo-Sung Ho, "Occlusion Aware Reduced Angular Candidates based Light Field Depth Estimation from an Epipolar Plane Image" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Processing: Algorithms and Systems XVI, 2018, pp 390-1 - 390-6, https://doi.org/10.2352/ISSN.2470-1173.2018.13.IPAS-390