  Synthesizing a densely sampled light field from a single image is highly
beneficial for many applications. Moreover, jointly solving both angular and
spatial super-resolution problem also introduces new possibilities in light
field imaging. The conventional method relies on physical-based rendering and a
secondary network to solve the angular super-resolution problem. In addition,
pixel-based loss limits the network capability to infer scene geometry
globally. In this paper, we show that both super-resolution problems can be
solved jointly from a single image by proposing a single end-to-end deep neural
network that does not require a physical-based approach. Two novel loss
functions based on known light field domain knowledge are proposed to enable
the network to preserve the spatio-angular consistency between sub-aperture
images. Experimental results show that the proposed model successfully
synthesizes dense high resolution light field and it outperforms the
state-of-the-art method in both quantitative and qualitative criteria. The
method can be generalized to arbitrary scenes, rather than focusing on a
particular subject. The synthesized light field can be used for various
applications, such as depth estimation and refocusing.
