Geometric Occlusion Analysis in Depth Estimation Using Integral Guided Filter for Light-Field Image

摘要

Unlike traditional multi-view images, sampling in angular domain of light field images is distributed in different directions. Therefore, an angular sampling image (ASI), comprising of possible matching points extracted from each view, is available for each point. In this paper, we analyze the geometric relationship between ASIs and reference sub-aperture images, and then prove the occlusion boundary similarity. Based on the geometric relationship in extreme cases, we show that some points in ASI have higher reliability than other points for depth calculation. An integral guided filter is then built based on the sub-aperture image to predict occlusion probabilities in ASIs. The filter is independent of ASIs and has no requirement for high angular resolution so that it is easy to apply to the cost volume calculation. We integrate the filter into our depth estimation framework and other state-of-the-art depth estimation frameworks. Experimental results demonstrate that the proposed filter is more effective to occluded point detection in ASIs than other methods. Results from different data sets show that our method outperforms the existing state-of-the-art depth estimation methods, especially along occlusion boundaries.