  We consider the problem of high-dimensional light field reconstruction and
develop a learning-based framework for spatial and angular super-resolution.
Many current approaches either require disparity clues or restore the spatial
and angular details separately. Such methods have difficulties with
non-Lambertian surfaces or occlusions. In contrast, we formulate light field
super-resolution (LFSR) as tensor restoration and develop a learning framework
based on a two-stage restoration with 4-dimensional (4D) convolution. This
allows our model to learn the features capturing the geometry information
encoded in multiple adjacent views. Such geometric features vary near the
occlusion regions and indicate the foreground object border. To train a
feasible network, we propose a novel normalization operation based on a group
of views in the feature maps, design a stage-wise loss function, and develop
the multi-range training strategy to further improve the performance.
Evaluations are conducted on a number of light field datasets including
real-world scenes, synthetic data, and microscope light fields. The proposed
method achieves superior performance and less execution time comparing with
other state-of-the-art schemes.
