A major challenge for matching-based depth estimation is to prevent mismatches in occlusion and smooth regions. An effective matching window satisfying three characteristics: texture richness, disparity consistency and anti-occlusion should be able to prevent mismatches to some extent. According to these characteristics, we propose matching entropy in the spatial domain of light field to measure the amount of correct information in a matching window, which provides the criterion for matching window selection. Based on matching entropy regularization, we establish an optimization model for depth estimation with a matching cost fidelity term. To find the optimum, we propose a two-step adaptive matching algorithm. First, the region type is adaptively determined to identify occluding, occluded, smooth and textured regions. Then, the matching entropy criterion is used to adaptively select the size and shape of matching windows, as well as the visible viewpoints. The two-step process can reduce mismatches and redundant calculations by selecting effective matching windows. The experimental results on synthetic and real data show that the proposed method can effectively improve the accuracy of depth estimation in occlusion and smooth regions and has strong robustness for different noise levels. Therefore, high-precision depth estimation from 4D light field data is achieved.

翻译：基于匹配的深度估计面临的一个主要挑战是防止遮挡区域和平滑区域的误匹配。一个有效的匹配窗口应满足三个特性：纹理丰富度、视差一致性和抗遮挡性，这些特性能在一定程度上避免误匹配。针对这些特性，我们提出了光场空间域中的匹配熵概念，用于衡量匹配窗口中正确信息的数量，为匹配窗口选择提供了准则。基于匹配熵正则化，我们建立了一个包含匹配代价保真项的深度估计优化模型。为求解最优值，我们提出了一种两步自适应匹配算法：首先自适应确定区域类型，以识别遮挡区、被遮挡区、平滑区和纹理区；随后利用匹配熵准则自适应选择匹配窗口的尺寸、形状及可见视点。通过两步处理，该方法能选择有效匹配窗口，减少误匹配和冗余计算。在合成和真实数据上的实验结果表明，所提方法能有效提升遮挡区域和平滑区域的深度估计精度，并对不同噪声水平具有强鲁棒性，从而实现了从四维光场数据中获取高精度深度估计。