  Light field imaging introduced the capability to refocus an image after
capturing. Currently there are two popular methods for refocusing,
shift-and-sum and Fourier slice methods. Neither of these two methods can
refocus the light field in real-time without any pre-processing. In this paper
we introduce a machine learning based refocusing technique that is capable of
extracting 16 refocused images with refocusing parameters of
\alpha=0.125,0.250,0.375,...,2.0 in real-time. We have trained our network,
which is called RefNet, in two experiments. Once using the Fourier slice method
as the training -- i.e., "ground truth" -- data and another using the
shift-and-sum method as the training data. We showed that in both cases, not
only is the RefNet method at least 134x faster than previous approaches, but
also the color prediction of RefNet is superior to both Fourier slice and
shift-and-sum methods while having similar depth of field and focus distance
performance.
