# An Improved SIFT Underwater Image Stitching Method
## 2 Related Work  
In this section, we briefly review image stitching methods, both on land and underwater. While research on underwater image stitching has been relatively limited in recent years, notable advancements have been achieved in land-based image stitching methods. In land-based image stitching, a spectrum of approaches has been put forward to enhance local alignment. This includes the computation of multi-content-aware local warping as opposed to a singular global warping strategy. Moreover, some methodologies advocate for the use of seam-driven image stitching strategies aimed at mitigating artifacts resulting from image fusion [14; 15]. [PERSON] et al. [16] proposed the AutoStitch image stitching algorithm, which leverages a global single-response matrix to establish correspondences across all images and generate a panoramic result through multiband fusion. This approach enabled the automated stitching of multiple images. However, a critical constraint of the AutoStitch algorithm is its reliance on the assumption of a consistent scene depth across overlapping image areas. Deviations from this assumption can lead to pronounced ghosting and alignment errors. To mitigate this limitation, researchers have proposed employing more robust matrices or multiple single-response matrices in place of a single global one. [PERSON] et al. [17] proposed the smoothly varying affine (SVA) method, which incorporates smoothly varying affine stitching fields instead of global affine transformations. This technique exhibits superior local deformation and alignment capabilities and can also handle parallax to some extent. [PERSON] et al. [18] proposed the dual-homography warping (DHW) approach, which divides the scene into a background plane and a foreground plane. Two distinct single-response matrices are employed, each aligned to the background and foreground, facilitating seamless stitching of a broader range of realistic scenes. [PERSON] et al. [11] proposed As-Projective-As-Possible (APAPAP). Firstly, SIFT features are extracted from two images, and erroneous features are removed using RANSAC. Then, an efficient computational method, moving DLT, is given to estimate the global homography matrix. When two images exhibit variations beyond mere rotation, or when they depict nonplanar scenes, moving DLT employs a location-dependent homography to warp each matching point. In the APAP algorithm, the image is divided into dense grids according to a certain pattern. Higher weights are provided for closer feature points, and lower weights are provided for more distant feature points. Each grid is aligned with a local single-response matrix, which is calculated from the global single-response matrix and the grid weights. Finally, the image stitching is performed using the local homography matrix. The APAP algorithm can align multiple images better via local weight constraint adjustment. Based on this, many researchers have been inspired to propose the use of grid optimization to achieve image stitching. However, since APAP performs affine transformation in non-overlapping regions of the image as well, it can cause severe visual distortion. [PERSON] et al. [12] proposed shape-preserving half-projective (SPHP), which is based on the shape-preserving method of image scaling from the perspective of shape correction. The algorithm performs distorted alignment with single-strain transformations in overlapping regions and maintains the perspective of the image in non-overlapping regions with similarity transformations. At the same time, a smooth transition is formed between the overlapping and non-overlapping regions. The SPHP algorithm can reduce the perspective distortion in non-overlapping regions. The authors also proposed combining SPHP with APAP to make the proposed warping more robust. [PERSON] et al. [13] proposed adaptive As-Natural-As-Possible (AANAP). This method uses a smooth stitching field over the entire target image while taking into account all local transformation variations.