Depth Estimation with Occlusion Prediction in Light Field Images

This paper addresses the problem of depth estimation in light field images by handling occlusions in a robust way. Previous methods determine the occlusion maps either by using the edge information in the center view or by employing various cues based on disparity cost. Here we propose to determine the occlusions based on both disparity cost and edge information. The proposed method first gets a collective response from all the depth cues of the different views. Then it determines the occluded pixels by relying on the edge information in this collective response. Based on this predicted occlusion map, the resulting depth map is regularized by standard graph-cut optimization and filtered using weighted median filtering. Experimental results on synthetic light field images demonstrate the superiority of the proposed method compared to the state-of-the-art both in quantitatively and qualitatively.