  For dense sampled light field (LF) reconstruction problem, existing
approaches focus on a depth-free framework to achieve non-Lambertian
performance. However, they trap in the trade-off "either aliasing or blurring"
problem, i.e., pre-filtering the aliasing components (caused by the angular
sparsity of the input LF) always leads to a blurry result. In this paper, we
intend to solve this challenge by introducing an elaborately designed epipolar
plane image (EPI) structure within a learning-based framework. Specifically, we
start by analytically showing that decreasing the spatial scale of an EPI shows
higher efficiency in addressing the aliasing problem than simply adopting
pre-filtering. Accordingly, we design a Laplacian Pyramid EPI (LapEPI)
structure that contains both low spatial scale EPI (for aliasing) and
high-frequency residuals (for blurring) to solve the trade-off problem. We then
propose a novel network architecture for the LapEPI structure, termed as
LapEPI-net. To ensure the non-Lambertian performance, we adopt a
transfer-learning strategy by first pre-training the network with natural
images then fine-tuning it with unstructured LFs. Extensive experiments
demonstrate the high performance and robustness of the proposed approach for
tackling the aliasing-or-blurring problem as well as the non-Lambertian
reconstruction.
