  Light field (LF) cameras record both intensity and directions of light rays,
and encode 3D scenes into 4D LF images. Recently, many convolutional neural
networks (CNNs) have been proposed for various LF image processing tasks.
However, it is challenging for CNNs to effectively process LF images since the
spatial and angular information are highly inter-twined with varying
disparities. In this paper, we propose a generic mechanism to disentangle these
coupled information for LF image processing. Specifically, we first design a
class of domain-specific convolutions to disentangle LFs from different
dimensions, and then leverage these disentangled features by designing
task-specific modules. Our disentangling mechanism can well incorporate the LF
structure prior and effectively handle 4D LF data. Based on the proposed
mechanism, we develop three networks (i.e., DistgSSR, DistgASR and DistgDisp)
for spatial super-resolution, angular super-resolution and disparity
estimation. Experimental results show that our networks achieve
state-of-the-art performance on all these three tasks, which demonstrates the
effectiveness, efficiency, and generality of our disentangling mechanism.
Project page: https://yingqianwang.github.io/DistgLF/.
