  With the introduction of consumer light field cameras, light field imaging
has recently become widespread. However, there is an inherent trade-off between
the angular and spatial resolution, and thus, these cameras often sparsely
sample in either spatial or angular domain. In this paper, we use machine
learning to mitigate this trade-off. Specifically, we propose a novel
learning-based approach to synthesize new views from a sparse set of input
views. We build upon existing view synthesis techniques and break down the
process into disparity and color estimation components. We use two sequential
convolutional neural networks to model these two components and train both
networks simultaneously by minimizing the error between the synthesized and
ground truth images. We show the performance of our approach using only four
corner sub-aperture views from the light fields captured by the Lytro Illum
camera. Experimental results show that our approach synthesizes high-quality
images that are superior to the state-of-the-art techniques on a variety of
challenging real-world scenes. We believe our method could potentially decrease
the required angular resolution of consumer light field cameras, which allows
their spatial resolution to increase.
