  Light field (LF) imaging, which captures both spatial and angular information
of a scene, is undoubtedly beneficial to numerous applications. Although
various techniques have been proposed for LF acquisition, achieving both
angularly and spatially high-resolution LF remains a technology challenge. In
this paper, a learning-based approach applied to 3D epipolar image (EPI) is
proposed to reconstruct high-resolution LF. Through a 2-stage super-resolution
framework, the proposed approach effectively addresses various LF
super-resolution (SR) problems, i.e., spatial SR, angular SR, and
angular-spatial SR. While the first stage provides flexible options to
up-sample EPI volume to the desired resolution, the second stage, which
consists of a novel EPI volume-based refinement network (EVRN), substantially
enhances the quality of the high-resolution EPI volume. An extensive evaluation
on 90 challenging synthetic and real-world light field scenes from 7 published
datasets shows that the proposed approach outperforms state-of-the-art methods
to a large extend for both spatial and angular super-resolution problem, i.e.,
an average peak signal to noise ratio improvement of more than 2.0 dB, 1.4 dB,
and 3.14 dB in spatial SR $\times 2$, spatial SR $\times 4$, and angular SR
respectively. The reconstructed 4D light field demonstrates a balanced
performance distribution across all perspective images and presents superior
visual quality compared to the previous works.
