http://ieeexplore.ieee.org/document/7856946/Commercial light field cameras provide spatial and angular information, but their limited resolution becomes an
important problem in practical use. In this paper, we present a novel method for light field image super-resolution (SR) to simultaneously
up-sample both the spatial and angular resolutions of a light field image via a deep convolutional neural network. We
first augment the spatial resolution of each sub-aperture image by a spatial SR network, then novel views between super-resolved
sub-aperture images are generated by three different angular SR networks according to the novel view locations. We improve both
the efficiency of training and the quality of angular SR results by using weight sharing. In addition, we provide a new light
field image dataset for training and validating the network. We train our whole network end-to-end, and show state-of-the-art
performances on quantitative and qualitative evaluations