  Background objects occluded in some views of a light field (LF) camera can be
seen by other views. Consequently, occluded surfaces are possible to be
reconstructed from LF images. In this paper, we handle the LF de-occlusion
(LF-DeOcc) problem using a deep encoder-decoder network (namely, DeOccNet). In
our method, sub-aperture images (SAIs) are first given to the encoder to
incorporate both spatial and angular information. The encoded representations
are then used by the decoder to render an occlusionfree center-view SAI. To the
best of our knowledge, DeOccNet is the first deep learning-based LF-DeOcc
method. To handle the insufficiency of training data, we propose an LF
synthesis approach to embed selected occlusion masks into existing LF images.
Besides, several synthetic and realworld LFs are developed for performance
evaluation. Experimental results show that, after training on the generated
data, our DeOccNet can effectively remove foreground occlusions and achieves
superior performance as compared to other state-of-the-art methods. Source
codes are available at: https://github.com/YingqianWang/DeOccNet.
