  Light-field cameras (LFC) have received increasing attention due to their
wide-spread applications. However, current LFCs suffer from the well-known
spatio-angular trade-off, which is considered as an inherent and fundamental
limit for LFC designs. In this paper, by doing a detailed geometrical optical
analysis of the sampling process in an LFC, we show that the effective sampling
resolution is generally higher than the number of micro-lenses. This
contribution makes it theoretically possible to break the resolution trade-off.
Our second contribution is an epipolar plane image (EPI) based super-resolution
method, which can super-resolve the spatial and angular dimensions
simultaneously. We prove that the light field is a 2D series, thus, a
specifically designed CNN-LSTM network is proposed to capture the continuity
property of the EPI. Rather than leveraging semantic information, our network
focuses on extracting geometric continuity in the EPI. This gives our method an
improved generalization ability and makes it applicable to a wide range of
previously unseen scenes. Experiments on both synthetic and real light fields
demonstrate the improvements over state-of-the-art, especially in large
disparity areas.
