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Unsupervised Learning of High-resolution Light Field Imaging via Beam Splitter-based Hybrid Lenses

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.

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