  A light field records numerous light rays from a real-world scene. However,
capturing a dense light field by existing devices is a time-consuming process.
Besides, reconstructing a large amount of light rays equivalent to multiple
light fields using sparse sampling arises a severe challenge for existing
methods. In this paper, we present a learning based method to reconstruct
multiple novel light fields between two mutually independent light fields. We
indicate that light rays distributed in different light fields have the same
consistent constraints under a certain condition. The most significant
constraint is a depth related correlation between angular and spatial
dimensions. Our method avoids working out the error-sensitive constraint by
employing a deep neural network. We solve residual values of pixels on epipolar
plane image (EPI) to reconstruct novel light fields. Our method is able to
reconstruct 2 to 4 novel light fields between two mutually independent input
light fields. We also compare our results with those yielded by a number of
alternatives elsewhere in the literature, which shows our reconstructed light
fields have better structure similarity and occlusion relationship.
