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. 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. 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%. 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.