  This paper explores the problem of reconstructing high-resolution light field
(LF) images from hybrid lenses, including a high-resolution camera surrounded
by multiple low-resolution cameras. To tackle this challenge, we propose a
novel end-to-end learning-based approach, which can comprehensively utilize the
specific characteristics of the input from two complementary and parallel
perspectives. Specifically, one module regresses a spatially consistent
intermediate estimation by learning a deep multidimensional and cross-domain
feature representation; the other one constructs another intermediate
estimation, which maintains the high-frequency textures, by propagating the
information of the high-resolution view. We finally leverage the advantages of
the two intermediate estimations via the learned attention maps, leading to the
final high-resolution LF image. Extensive experiments demonstrate the
significant superiority of our approach over state-of-the-art ones. That is,
our method not only improves the PSNR by more than 2 dB, but also preserves the
LF structure much better. To the best of our knowledge, this is the first
end-to-end deep learning method for reconstructing a high-resolution LF image
with a hybrid input. We believe our framework could potentially decrease the
cost of high-resolution LF data acquisition and also be beneficial to LF data
storage and transmission. The code is available at
https://github.com/jingjin25/LFhybridSR-Fusion.
