Occlusion removal in an image can aid in facilitating the robustness of numerous computer vision tasks, e.g., detection and tracking in surveillance. However, the invisible property of contents behind the occlusions limits occlusion removal from the single view. Recently, the emerging light field (LF) data, which contains rich multi-view perception of the scene, provides potential solution for this challenge. To better exploit the capability of occlusion location and occluded contents recovery from LF data, in this paper, we propose a LF occlusion removal network (LFORNet), which consists of three key sub-networks: the foreground occlusion location (FOL) sub-network, the background content recovery (BCR) sub-network, and the refinement sub-network. Specifically, both FOL sub-network and BCR sub-network explore the multi-view information of LF data, and thus they are constructed with the same network structure to estimate the occlusion mask and the coarse occluded contents map, respectively. The refinement sub-network aggregates the above two outputs to obtain the refined occlusion removal. Meanwhile, we use multi-angle view stacks as the input of the network, which can make full use of the inherent information among the LF views. Experimental results show that our method is suitable for different sizes of occlusions, and surpasses the state-of-the-art approaches in both synthetic and real-world scenes.

图像中的遮挡去除有助于促进众多计算机视觉任务的鲁棒性，例如。G., 监视中的检测和跟踪。然而，遮挡背后内容的不可见属性限制了从单一视图中移除遮挡。最近，包含丰富的场景多视图感知的新兴光场（LF）数据为这一挑战提供了潜在的解决方案。为了更好地利用 LF 数据中的遮挡定位和遮挡内容恢复能力，本文提出了一种 LF 遮挡去除网络 (LFORNet)，它由三个关键子网络组成：前景遮挡位置 (FOL) 子网络，背景内容恢复（BCR）子网络和细化子网络。具体来说，FOL 子网络和 BCR 子网络都探索 LF 数据的多视图信息，因此它们是用相同的网络结构构造的，分别估计遮挡掩码和粗略遮挡内容图。细化子网络聚合上述两个输出以获得细化的遮挡去除。同时，我们使用多角度视图堆栈作为网络的输入，可以充分利用 LF 视图之间的固有信息。实验结果表明，我们的方法适用于不同大小的遮挡，并且在合成场景和真实世界场景中都超过了最先进的方法。