  In this paper, we design a beam splitter-based hybrid light field imaging
prototype to record 4D light field image and high-resolution 2D image
simultaneously, and make a hybrid light field dataset. The 2D image could be
considered as the high-resolution ground truth corresponding to the
low-resolution central sub-aperture image of 4D light field image.
Subsequently, we propose an unsupervised learning-based super-resolution
framework with the hybrid light field dataset, which adaptively settles the
light field spatial super-resolution problem with a complex degradation model.
Specifically, we design two loss functions based on pre-trained models that
enable the super-resolution network to learn the detailed features and light
field parallax structure with only one ground truth. Extensive experiments
demonstrate the same superiority of our approach with supervised learning-based
state-of-the-art ones. To our knowledge, it is the first end-to-end
unsupervised learning-based spatial super-resolution approach in light field
imaging research, whose input is available from our beam splitter-based hybrid
light field system. The hardware and software together may help promote the
application of light field super-resolution to a great extent.
