## 1 Introduction  
Image registration is a process of image matching and transformation of two or more different images. It is widely used in such fields as panoramic image splicing [1; 2], high dynamic range imaging [3], simultaneous localization and mapping (SLAM) [4], and so on. Traditional image registration algorithms are mainly classified into pixel-based algorithms and feature-based algorithms [5; 6]. In pixel-based image registration algorithms, the original pixel values are directly used to estimate the transformation relationship between images [7; 8]. Firstly, the homography matrix between a pair of images is initialized. Then, the homography matrix is used to transform the image, and the errors of pixel values of the transformed image are calculated. Finally, the optimization technique is used to minimize the error function to achieve image registration. The pixel-based algorithms usually run slowly and are effective to low-texture scenes, but have poor robustness to scale, rotation and brightness. In feature-based image registration algorithms [9; 10] such as SIFT [11], ORB [12], etc., feature points of images are generally extracted first, and the corresponding relationship between feature points of the two images is established by feature matching, and the optimal homography matrix is estimated by algorithms such as RANSAC [13], etc. Feature-based image registration algorithms are generally better and faster than pixel-based image registration, but feature-based algorithms require that there must be enough matching points between the two images and that the accuracy of matching points is higher and the location distribution of matching points is uniform. Otherwise, the registration accuracy will be greatly reduced. Feature-based image registration algorithms generally have good robustness to scale and rotation and have robustness to brightness to some extent, but are not suitable for low-texture images. Recently, some deep learning-based image registration algorithms have been proposed. [PERSON] et al. [14] proposed a homography matrix estimation algorithm with supervised learning. A 128 \(\times\) 128 image \(I_{A}\) was generated by randomly clipping from an image \(I\), and then random perturbation values were added to the coordinates of the four corners of the image \(I_{A}\) to generate four perturbation points, so that four pairs of matching points were obtained. The homography matrix corresponding to the four pairs of points was calculated by using the coordinates of the four corners of image \(I_{A}\) and their corresponding perturbation points. The homography matrix was used to transform image \(I_{A}\) into image \(I_{B}\). Then, the images \(I_{A}\) and \(I_{B}\) were converted into grayscale images as samples, and the coordinate differences between the four corner points of \(I_{A}\) and their corresponding perturbation points in \(I_{B}\) were used as labels, with which a 10-layer VGG (Visual Geometry Group) network was trained, and finally a homography matrix estimation model that could be used for image registration was obtained. The algorithm has better robustness to brightness, scale, rotation, and texture. On the basis of [PERSON]'s work, [PERSON] et al. [15] proposed a homography matrix estimation algorithm with unsupervised learning to solve the shortcoming of artificially generated labels in supervised learning, but this algorithm had weak robustness to illumination. The samples used in these two algorithms were mainly artificially generated samples. The artificial samples ensured that the accuracy of the samples and labels was high enough, which was a beneficial exploration for deep learning to solve the actual image registration problem. However, the artificial samples adopted by these two works default to no parallax between the images to be registered, so only four pairs of corresponding points are used to represent the registration relationship between the two images.