  The fusion of images from dual camera systems featuring a wide-angle and a
telephoto camera has become a hotspot problem recently. By integrating
simultaneously captured wide-angle and telephoto images from these systems, the
resulting fused image achieves a wide field of view (FOV) coupled with
high-definition quality. Existing approaches are mostly deep learning methods,
and predominantly rely on supervised learning, where the training dataset plays
a pivotal role. However, current datasets typically adopt a data synthesis
approach generate input pairs of wide-angle and telephoto images alongside
ground-truth images. Notably, the wide-angle inputs are synthesized rather than
captured using real wide-angle cameras, and the ground-truth image is captured
by wide-angle camera whose quality is substantially lower than that of input
telephoto images captured by telephoto cameras. To address these limitations,
we introduce a novel hardware setup utilizing a beam splitter to simultaneously
capture three images, i.e. input pairs and ground-truth images, from two
authentic cellphones equipped with wide-angle and telephoto dual cameras.
Specifically, the wide-angle and telephoto images captured by cellphone 2 serve
as the input pair, while the telephoto image captured by cellphone 1, which is
calibrated to match the optical path of the wide-angle image from cellphone 2,
serves as the ground-truth image, maintaining quality on par with the input
telephoto image. Experiments validate the efficacy of our newly introduced
dataset, named ReWiTe, significantly enhances the performance of various
existing methods for real-world wide-angle and telephoto dual image fusion
tasks.
