  Restoring a sharp light field image from its blurry input has become
essential due to the increasing popularity of parallax-based image processing.
State-of-the-art blind light field deblurring methods suffer from several
issues such as slow processing, reduced spatial size, and a limited motion blur
model. In this work, we address these challenging problems by generating a
complex blurry light field dataset and proposing a learning-based deblurring
approach. In particular, we model the full 6-degree of freedom (6-DOF) light
field camera motion, which is used to create the blurry dataset using a
combination of real light fields captured with a Lytro Illum camera, and
synthetic light field renderings of 3D scenes. Furthermore, we propose a light
field deblurring network that is built with the capability of large receptive
fields. We also introduce a simple strategy of angular sampling to train on the
large-scale blurry light field effectively. We evaluate our method through both
quantitative and qualitative measurements and demonstrate superior performance
compared to the state-of-the-art method with a massive speedup in execution
time. Our method is about 16K times faster than Srinivasan et. al. [22] and can
deblur a full-resolution light field in less than 2 seconds.
