  Traditional feature-based image stitching technologies rely heavily on
feature detection quality, often failing to stitch images with few features or
low resolution. The learning-based image stitching solutions are rarely studied
due to the lack of labeled data, making the supervised methods unreliable. To
address the above limitations, we propose an unsupervised deep image stitching
framework consisting of two stages: unsupervised coarse image alignment and
unsupervised image reconstruction. In the first stage, we design an
ablation-based loss to constrain an unsupervised homography network, which is
more suitable for large-baseline scenes. Moreover, a transformer layer is
introduced to warp the input images in the stitching-domain space. In the
second stage, motivated by the insight that the misalignments in pixel-level
can be eliminated to a certain extent in feature-level, we design an
unsupervised image reconstruction network to eliminate the artifacts from
features to pixels. Specifically, the reconstruction network can be implemented
by a low-resolution deformation branch and a high-resolution refined branch,
learning the deformation rules of image stitching and enhancing the resolution
simultaneously. To establish an evaluation benchmark and train the learning
framework, a comprehensive real-world image dataset for unsupervised deep image
stitching is presented and released. Extensive experiments well demonstrate the
superiority of our method over other state-of-the-art solutions. Even compared
with the supervised solutions, our image stitching quality is still preferred
by users.
