Robust depth estimation for multi-occlusion in light-field images

Occlusion is one of the most important issues in light-field depth estimation. In this paper, we propose a light-field multi-occlusion model with the analysis of light transmission. By the model, occlusions in different views are discussed separately. An adaptive algorithm of anti-occlusion in the central view is proposed to obtain more precise consistency regions (unoccluded views) in the angular domain and a subpatch approach of anti-occlusion in other views is presented to optimize the initial depth maps, where depth boundaries are better preserved. Then we propose a curvature confidence analysis approach to make depth evaluation more accurate and it is designed in an energy model to regularize the depth maps. Experimental results demonstrate that the proposed algorithm achieves better subjective and objective quality in depth maps compared with state-of-the-art algorithms.