# Utilization of Merge-Sorting Method to Improve Stitching Efficiency in Multi-Scene Image Stitching
## 1 Introduction
[20] increased the number of surface matching points for objects in images by using adaptive histogram equalization and used the improved zero-mean normalized cross-correlation (ZNCC) algorithm to reduce the false matching rate for feature points and improve the matching efficiency. [PERSON] et al. [21] used the effective part of second-order minimization (ESM) to calculate geometric errors and reduce ghosting and distortion, employing interpolation between homography and similarity transformations with a general matrix manifold. [PERSON] et al. [22] used a Gaussian pyramid combined with a Laplacian pyramid to fuse images. The abovementioned methods for image feature point detection, image feature point matching, and image fusion are mainly used in the stitching of ordered images, and there are few studies on methods for unordered image stitching. If the stitching method used with ordered images is directly applied to the stitching of unordered images, it will have a great impact on the image feature point matching and fusion, and some may even result in errors and not be able to perform image fusion. Even if image fusion can be performed, the final stitched result may be incomplete, and the information from the two images is lost. In severe cases, the result of stitching may be completely black or the image may be severely distorted, and the information from the image will not be recognizable at all. Unordered image stitching is very different from ordered image stitching. Ordered images are pairs of adjacent images that have overlapping areas. When stitching, the adjacent two images can be directly stitched, and the stitching order goes from left to right. Unordered images are pairs of adjacent images that may or may not have overlapping areas. Two images with overlapping areas need to be detected before stitching, and the stitching order is not fixed. Thus, compared with ordered image stitching, the efficiency of unordered image stitching is lower and the process is more time-consuming. In order to achieve the stitching of unordered images, the traditional method involves finding image pairs in an unordered image set for which feature point matching is possible and then sorting the matched images before performing image feature point matching and image fusion. Although the stitching of unordered images can be achieved, the stitching efficiency is low and the process is time-consuming. The most time-consuming stage is finding all the image pairs that can be matched. [PERSON] et al. [23] used an exhaustive matching method in which, for example, if there are N images, the method must match N(N-1)/2 image pairs. [PERSON] et al. [7] first estimated the overlapping areas of the images and then used the improved binary tree model to stitch the unordered images. The above two methods for stitching unordered images can both complete the stitching and produce good results, and they also improve the stitching efficiency compared to the traditional methods to a certain extent. However, both methods need to traverse all the images or estimate all their overlapping areas before performing image fusion. In this way, the problems of the process being time-consuming and having low efficiency arise. In order to solve the problems of low efficiency and a time-consuming process that arise with these methods, we propose a multi-scene image stitching method based on the merge-sorting method (merge-sorting image stitching (MSIS)). Our main contributions are as follows: (1) We develop an improved model based on the merge-sorting method to perform feature point matching and image stitching with unordered images in order to finally obtain an image of the entire scene. Compared with traditional methods, this method reduces the time required for unordered image matching and fusion and improves the stitching efficiency for unordered images.