Light field cameras capture the 3D information in a scene with a single
exposure. This special feature makes light field cameras very appealing for a
variety of applications: from post-capture refocus, to depth estimation and
image-based rendering. However, light field cameras suffer by design from
strong limitations in their spatial resolution, which should therefore be
augmented by computational methods. On the one hand, off-the-shelf single-frame
and multi-frame super-resolution algorithms are not ideal for light field data,
as they do not consider its particular structure. On the other hand, the few
super-resolution algorithms explicitly tailored for light field data exhibit
significant limitations, such as the need to estimate an explicit disparity map
at each view. In this work we propose a new light field super-resolution
algorithm meant to address these limitations. We adopt a multi-frame alike
super-resolution approach, where the complementary information in the different
light field views is used to augment the spatial resolution of the whole light
field. We show that coupling the multi-frame approach with a graph regularizer,
that enforces the light field structure via nonlocal self similarities, permits
to avoid the costly and challenging disparity estimation step for all the
views. Extensive experiments show that the new algorithm compares favorably to
the other state-of-the-art methods for light field super-resolution, both in
terms of PSNR and visual quality.Comment: This new version includes more material. In particular, we added: a
new section on the computational complexity of the proposed algorithm,
experimental comparisons with a CNN-based super-resolution algorithm, and new
experiments on a third datase
