# Utilization of Merge-Sorting Method to Improve Stitching Efficiency in Multi-Scene Image Stitching
## 1 Introduction  
In recent years, computer vision technology has developed rapidly, and image technology has been applied in various industries. Image technology is widely used for VR and 360 degree panoramic images. Image stitching technology is particularly important for panoramic images, and a lot of research has been carried out on image stitching algorithms. Image stitching algorithms stitch small parts of scenes together to obtain a picture of the whole scene. They are widely used in various fields, including for 3D reconstruction of objects [1], medical image analysis in medicine [2], remote sensing image analysis in geography [3], and 360 degree panoramic views outside cars in autonomous driving [4]. At present, the technology mainly focuses on the stitching of ordered images, and there is very little research on unordered image stitching. After consulting a large number of studies, it was found that unordered image stitching is similar to ordered image stitching, which involves stitching multiple images with overlapping areas into a panoramic image with a wide viewing angle and high resolution [5, 6]. This technique is suitable not only for stitching ordered images but also for unordered images. The main steps employed by image stitching technology can be divided into image preprocessing, image matching and registration, and image fusion, with the latter two steps being the core steps [7]. Image preprocessing involves inputting the images that need to be stitched into the model to prepare for subsequent image matching and fusion. Image matching is the process of determining the overlapping area between two or more images through feature point matching. The accuracy and speed of feature point matching directly affect the results and efficiency of image stitching. Image registration is the process of aligning two or more images after image matching into a single wide-field image. During the registration process, wrongly matching points are removed with the matching method, and the correctly matching points are retained to ensure that the images are stitched together nicely. Image fusion is the process of fusing the registered images into a wide-field image using an image fusion method, which directly determines the final stitching result. If the image fusion method used in this process is not suitable, the final stitching result will not achieve the desired effect, resulting in the failure of image stitching. The more commonly used feature point detection methods include scale-invariant feature transform (SIFT) [8], accelerated robust function (SURF) [9], oriented FAST and rotated BRIEF (ORB) [10], and A-KAZE [11]. After the feature points of the images are detected, it is necessary to match the detected feature points and then remove the incorrect feature points. Methods for image feature point matching include the brute force (BF), fast library of approximate nearest neighbors (FLANN), K-nearest neighbor (KNN) matcher, and filter random sample consensus (RANSAC) [12; 13] methods. Image fusion methods include linear weighted fusion, Laplacian pyramid fusion [14], and optimal suture fusion [15; 16]. In recent years, research on sequential image stitching has progressed rapidly, and many improved methods for image registration and fusion have been proposed. [PERSON] et al. [17] used a combination of SURF optimization and improved cell-accelerated power function weighting to eliminate erroneous matching points in image matching. [PERSON] et al. [18] further purified the feature points by using the sampling consistency algorithm to match the feature points. [PERSON] et al. [19] reduced the complexity of feature point descriptor data by using the ORB algorithm and principal component analysis (PCA) to process feature points. [PERSON] et al.