Anti-Noise Light Field Depth Estimation Using Inline Occlusion Handling

The light field camera records spatial and angular information of a scene within one shot, which is a unique advantage, especially for depth estimation. Both occlusion and noise make it difficult to estimate the depth of the light field. To address this problem, we propose a depth estimation method using inline occlusion handling, which uses digital refocusing to obtain refocused images and define occlusion types in scenes. An inline occlusion framework is used to construct the correlation cost. In order to remove noise, we use a variety of filtering strategies to optimize the cost. The quantitative results show that, compared with the best performance constrained angular entropy cost (CAE), our method can reduce the mean square error (MSE) and bad point rate by 40.68% and 25.76%, respectively, in the challenging noise scenes of old Heidelberg Collaboratory for Image Processing (HCI) datasets. In new HCI datasets, the reduction is 34.98% and 31.26%, respectively. The qualitative results show that we can preserve various fine structures of the real light field. Therefore, our method has significant advantages in high-noise scenes, which can better deal with the occlusion problem of depth estimation in noisy scenes.