  Light field imaging has recently known a regain of interest due to the
availability of practical light field capturing systems that offer a wide range
of applications in the field of computer vision. However, capturing
high-resolution light fields remains technologically challenging since the
increase in angular resolution is often accompanied by a significant reduction
in spatial resolution. This paper describes a learning-based spatial light
field super-resolution method that allows the restoration of the entire light
field with consistency across all sub-aperture images. The algorithm first uses
optical flow to align the light field and then reduces its angular dimension
using low-rank approximation. We then consider the linearly independent columns
of the resulting low-rank model as an embedding, which is restored using a deep
convolutional neural network (DCNN). The super-resolved embedding is then used
to reconstruct the remaining sub-aperture images. The original disparities are
restored using inverse warping where missing pixels are approximated using a
novel light field inpainting algorithm. Experimental results show that the
proposed method outperforms existing light field super-resolution algorithms,
achieving PSNR gains of 0.23 dB over the second best performing method. This
performance can be further improved using iterative back-projection as a
post-processing step.
