two Neither of these two methods 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 α = 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 134 × 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.