  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. The performance of existing methods is
still limited, as they produce either blurry results on plain textured areas or
distortions around depth discontinuous boundaries. 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, while the other module warps 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 adaptively via the learned
attention maps, leading to the final high-resolution LF image with satisfactory
results on both plain textured areas and depth discontinuous boundaries.
Besides, to promote the effectiveness of our method trained with simulated
hybrid data on real hybrid data captured by a hybrid LF imaging system, we
carefully design the network architecture and the training strategy. Extensive
experiments on both real and simulated hybrid data demonstrate the significant
superiority of our approach over state-of-the-art ones. To the best of our
knowledge, this is the first end-to-end deep learning method for LF
reconstruction from a real hybrid input. We believe our framework could
potentially decrease the cost of high-resolution LF data acquisition and
benefit LF data storage and transmission.
