  In this paper, a novel convolutional neural network (CNN)-based framework is
developed for light field reconstruction from a sparse set of views. We
indicate that the reconstruction can be efficiently modeled as angular
restoration on an epipolar plane image (EPI). The main problem in direct
reconstruction on the EPI involves an information asymmetry between the spatial
and angular dimensions, where the detailed portion in the angular dimensions is
damaged by undersampling. Directly upsampling or super-resolving the light
field in the angular dimensions causes ghosting effects. To suppress these
ghosting effects, we contribute a novel "blur-restoration-deblur" framework.
First, the "blur" step is applied to extract the low-frequency components of
the light field in the spatial dimensions by convolving each EPI slice with a
selected blur kernel. Then, the "restoration" step is implemented by a CNN,
which is trained to restore the angular details of the EPI. Finally, we use a
non-blind "deblur" operation to recover the spatial high frequencies suppressed
by the EPI blur. We evaluate our approach on several datasets, including
synthetic scenes, real-world scenes and challenging microscope light field
data. We demonstrate the high performance and robustness of the proposed
framework compared with state-of-the-art algorithms. We further show extended
applications, including depth enhancement and interpolation for unstructured
input. More importantly, a novel rendering approach is presented by combining
the proposed framework and depth information to handle large disparities.
