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.

利用基于张量恢复的学习框架进行高维光场重建和超分辨率，并通过多范围训练策略，提出一种新的归一化操作和阶段性损失函数来改善性能。在多个光场数据集中，该方法实现了优越的性能和更少的执行时间。