  Image stitching is a classical and crucial technique in computer vision,
which aims to generate the image with a wide field of view. The traditional
methods heavily depend on the feature detection and require that scene features
be dense and evenly distributed in the image, leading to varying ghosting
effects and poor robustness. Learning methods usually suffer from fixed view
and input size limitations, showing a lack of generalization ability on other
real datasets. In this paper, we propose an image stitching learning framework,
which consists of a large-baseline deep homography module and an edge-preserved
deformation module. First, we propose a large-baseline deep homography module
to estimate the accurate projective transformation between the reference image
and the target image in different scales of features. After that, an
edge-preserved deformation module is designed to learn the deformation rules of
image stitching from edge to content, eliminating the ghosting effects as much
as possible. In particular, the proposed learning framework can stitch images
of arbitrary views and input sizes, thus contribute to a supervised deep image
stitching method with excellent generalization capability in other real images.
Experimental results demonstrate that our homography module significantly
outperforms the existing deep homography methods in the large baseline scenes.
In image stitching, our method is superior to the existing learning method and
shows competitive performance with state-of-the-art traditional methods.
