  Axial light field resolution refers to the ability to distinguish features at
different depths by refocusing. The axial refocusing precision corresponds to
the minimum distance in the axial direction between two distinguishable
refocusing planes. High refocusing precision can be essential for some light
field applications like microscopy. In this paper, we propose a learning-based
method to extrapolate novel views from axial volumes of sheared epipolar plane
images (EPIs). As extended numerical aperture (NA) in classical imaging, the
extrapolated light field gives re-focused images with a shallower depth of
field (DOF), leading to more accurate refocusing results. Most importantly, the
proposed approach does not need accurate depth estimation. Experimental results
with both synthetic and real light fields show that the method not only works
well for light fields with small baselines as those captured by plenoptic
cameras (especially for the plenoptic 1.0 cameras), but also applies to light
fields with larger baselines.
