# An Improved SIFT Underwater Image Stitching Method
## 2 Related Work
It is less dependent on the choice of parameters and can automatically compute the appropriate global similarity transform. The AANAP algorithm can also mitigate the perspective distortion in non-overlapping regions by linearizing the homography and slowly changing it into a global similarity transform. [PERSON] et al. [19] developed an effective method to find a local alignment for optimal image stitching. This local stitching method uses a hybrid alignment model that simultaneously employs homography and content-preserving warping. [PERSON] et al. [20] proposed a parallax-tolerant image stitching method based on robust elastic warping. They constructed an analysis warping function to eliminate parallax errors. Then, the input images are warped over the meshed image plane according to the calculated deformations. However, the effectiveness of these land-based methods in stitching images with blurry underwater conditions remains to be demonstrated. Due to the complexity of the underwater environment, underwater image stitching techniques pose significant challenges. For more than thirty years, researchers have been studying to create automatic mosaics for underwater applications. For underwater mosaicking, some studies focus on vision-based navigation, while others focus on building large mosaics [21]. Some algorithms can achieve image stitching but require assistance from external detectors or have specific requirements for underwater image acquisition. Fortunately, SIFT-based stitching methods can stitch images using only image information, but they require a high number and accuracy of feature matches [3]. Insufficient feature matching can lead to stitching errors. One of the earliest computer-assisted underwater image stitching systems was proposed by [PERSON] [22] in 1986. This is a manual system that uses camera positions to directly obtain the position relationship and coordinates of adjacent frames. The motion parameters between frames can be computed directly from the camera positions, without requiring any registration. However, this system is not automatic or real-time. [PERSON] et al. [6] developed a method for estimating camera motion in real time directly from the luminance variations of the image sequence. In [23], direct estimates of motion were successfully applied in underwater mosaic schemes. To achieve video mosaics along unconstrained vehicle paths, [PERSON] et al. [24] utilized optimal estimation theory and smoother-follower techniques to identify the errors and reduce the image alignment errors propagating through the image chain. In [25], [PERSON] et al. extended the theory of smoother-follower estimation to achieve real-time creation and online improvement of unconstrained underwater video mosaics. However, the image estimates depend on the sensor data and the system geometry. [PERSON] et al. [7] used real-time image registration to achieve real-time creation of underwater video mosaics. Relative offsets between a pair of images can be used to determine when a new image should be acquired and where it belongs in the mosaic. In order to obtain images suitable for creating mosaics, the images are acquired snapping at a fixed time interval, snapping at a fixed vision interval. [PERSON] et al. [26] proposed an integrated approach to solve the problem of pose estimation and mosaic construction in underwater mosaicking. This method uses a simple image motion model to compute the image motion and focus on the use of long image sequences with temporal distance overlays, such as those generated by circular trajectories or zigzag scanning patterns. However, this approach is based on the assumption that the dominant motion existing between consecutive frames is translation, and it does not account for lens distortion. However, these approaches are unable to construct underwater mosaics without detector information or on arbitrarily obtained underwater images.