目的 光场相机通过一次成像同时记录场景的空间信息和角度信息，获取多视角图像和重聚焦图像，在深度估计中具有独特优势。遮挡是光场深度估计中的难点问题之一，现有方法没有考虑遮挡或仅仅考虑单一遮挡情况，对于多遮挡场景点，方法失效。针对遮挡问题，在多视角立体匹配框架下，提出了一种对遮挡鲁棒的光场深度估计算法。方法 首先利用数字重聚焦算法获取重聚焦图像，定义场景的遮挡类型，并构造相关性成本量。然后根据最小成本原则自适应选择最佳成本量，并求解局部深度图。最后利用马尔可夫随机场结合成本量和平滑约束，通过图割算法和加权中值滤波获取全局优化深度图，提升深度估计精度。结果 实验在HCI合成数据集和Stanford Lytro Illum实际场景数据集上展开，分别进行局部深度估计与全局深度估计实验。实验结果表明，相比其他先进方法，本文方法对遮挡场景效果更好，均方误差平均降低约26.8%。结论 本文方法能够有效处理不同遮挡情况，更好地保持深度图边缘信息，深度估计结果更准确，且时效性更好。此外，本文方法适用场景是朗伯平面场景，对于含有高光的非朗伯平面场景存在一定缺陷。

Objective A light-field camera can record space and angular information of a scene within one shot. The space information reflects the position of scene while the angular information reveals the views of scene. Multi-view and refocusing images can be obtained from light-field cameras, which possess unique advantage especially in depth estimation. Occlusion is a challenging issue for light-field depth estimation. Previous works have failed to model occlusion or have considered only single occlusion, thereby failing to achieve accurate depth for multi-occlusion. In this study, we present a light-field depth estimation algorithm that is robust to occlusion in a multi-view stereo matching framework. Method First, we apply the digital refocusing algorithm to obtain refocusing images. Then, we define occlusions into non-occlusion, single-occlusion, and multi-occlusion types. Given that different occlusions present dissimilar properties, we build the corresponding cost volume with refocusing images based on different occlusion types. Thereafter, we choose the optimal cost volume and calculate the local depth map in accordance with the min-cost principle. Finally, we utilize the graph cut algorithm to optimize local depth results by combining the cost volume and the smoothness constraint in a Markov random field framework to improve the accuracy of depth estimation. We apply the weighted median filter algorithm to remove noise and preserve the edge information of image. Result Experiments are conducted on the HCI synthetic dataset and Stanford Lytro Illum dataset for real scenes. The proposed approach works better for occluded scenes than do other state-of-the-art methods as its MSE decreases by approximately 26.8%. Conclusion Our approach obtains highly accurate edge-preserved depth map and is robust to different occlusion types. In addition, the running-time efficiency outperforms that of other methods. Although our approach performs well in the Lambertian scene, it may fail in non-Lambertian scene with glossy objects.