  Plenoptic cameras usually sacrifice the spatial resolution of their SAIs to
acquire geometry information from different viewpoints. Several methods have
been proposed to mitigate such spatio-angular trade-off, but seldom make use of
the structural properties of the light field (LF) data efficiently. In this
paper, we propose a novel high-order residual network to learn the geometric
features hierarchically from the LF for reconstruction. An important component
in the proposed network is the high-order residual block (HRB), which learns
the local geometric features by considering the information from all input
views. After fully obtaining the local features learned from each HRB, our
model extracts the representative geometric features for spatio-angular
upsampling through the global residual learning. Additionally, a refinement
network is followed to further enhance the spatial details by minimizing a
perceptual loss. Compared with previous work, our model is tailored to the rich
structure inherent in the LF, and therefore can reduce the artifacts near
non-Lambertian and occlusion regions. Experimental results show that our
approach enables high-quality reconstruction even in challenging regions and
outperforms state-of-the-art single image or LF reconstruction methods with
both quantitative measurements and visual evaluation.
