  As an image sensing instrument, light field images can supply extra angular
information compared with monocular images and have facilitated a wide range of
measurement applications. Light field image capturing devices usually suffer
from the inherent trade-off between the angular and spatial resolutions. To
tackle this problem, several methods, such as light field reconstruction and
light field super-resolution, have been proposed but leaving two problems
unaddressed, namely domain asymmetry and efficient information flow. In this
paper, we propose an end-to-end Spatio-Angular Dense Network (SADenseNet) for
light field reconstruction with two novel components, namely correlation blocks
and spatio-angular dense skip connections to address them. The former performs
effective modeling of the correlation information in a way that conforms with
the domain asymmetry. And the latter consists of three kinds of connections
enhancing the information flow within two domains. Extensive experiments on
both real-world and synthetic datasets have been conducted to demonstrate that
the proposed SADenseNet's state-of-the-art performance at significantly reduced
costs in memory and computation. The qualitative results show that the
reconstructed light field images are sharp with correct details and can serve
as pre-processing to improve the accuracy of related measurement applications.
