  Light field photography enables to record 4D images, containing angular
information alongside spatial information of the scene. One of the important
applications of light field imaging is post-capture refocusing. Current methods
require for this purpose a dense field of angle views; those can be acquired
with a micro-lens system or with a compressive system. Both techniques have
major drawbacks to consider, including bulky structures and angular-spatial
resolution trade-off. We present a novel implementation of digital refocusing
based on sparse angular information using neural networks. This allows
recording high spatial resolution in favor of the angular resolution, thus,
enabling to design compact and simple devices with improved hardware as well as
better performance of compressive systems. We use a novel convolutional neural
network whose relatively small structure enables fast reconstruction with low
memory consumption. Moreover, it allows handling without re-training various
refocusing ranges and noise levels. Results show major improvement compared to
existing methods.
