# An Improved SIFT Underwater Image Stitching Method
## 4 Results
### Qualitative Comparisons  
In this paper, we conducted a comparative analysis by selecting several representative image stitching algorithms. Specifically, we categorized the images into two groups (underwater images and land images) based on their content. Subsequently, we compared our method with four other approaches: traditional SIFT, APAP, SPHP, and AANAP. To facilitate alignment comparison across these stitching methods, we employed a straightforward pixel-weighted average fusion technique. To maintain conciseness, we present the results for the Temple [11] and Rock datasets only. Figures 3 and 4 depict the stitching results for the Rock and Temple images, respectively. The Rock dataset was obtained from the internet, while the Temple dataset was provided by the authors of [18]. As observed in the figures, the results of the other methods exhibited varying degrees of ghosting artifacts within the overlapping regions. In contrast, our method demonstrated superior performance in eliminating artifacts from these overlapping areas. If we focus our attention on the regions enclosed by the red ellipses in the figures, it becomes evident that our results offer a more natural appearance. Figure 3: Qualitative comparisons of image stitching on the Rock image pair. Red circles highlight errors. List of acronyms and initialisms: (**a**) SIFT, scale-invariant feature transform; (**b**) APAP, As-Projective-As-Possible warps; (**c**) AANAP, adaptive As-Natural-As-Possible; (**d**) SPHP, shape-preserving half-projective warps; (**e**) our method. Figure 3 illustrates the results for the Rock dataset, with each row representing the output of a different method. The sequence of results is as follows: traditional SIFT algorithm, APAP, AANAP, SPHP, and our method. The images' intricate textural features, particularly in underwater settings, present a substantial challenge for image stitching. We highlight two aspects for each result: red boxes indicate parallax errors in the overlapping regions where the feature textures are clear, and blue boxes indicate parallax errors in the overlapping regions where the feature textures are blurred. The outcomes of the traditional SIFT method exhibit low alignment accuracy, with ghosting effects appearing at the edges of the rocks, leading to a significant loss of detail information. The results in the second row indicate that APAP performs well in regions with relatively rich texture features but faces challenges in aligning areas with blurred details. Moving to the third row, we can observe the outcomes of the AANAP method, which exhibit noticeable splicing artifacts and alignment errors at the junction of overlapping and non-overlapping regions. In the fourth row, we present the results of the SPHP method. SPHP demonstrates effective alignment in regions with abundant texture features but encounters difficulties in regions characterized by blurred details. The results of the Temple dataset are presented in Figure 4, with each row corresponding to a different method. The order of the results is as follows: the traditional SIFT algorithm, APAP, AANAP, SPHP, and our method. Two aspects of each result have been high  
Figure 4: Qualitative comparisons of image stitching on the Temple image pair. Red circles highlight errors. List of acronyms and initialisms: (**a**) SIFT, scale-invariant feature transform; (**b**) APAP, As-Projective-As-Possible warps; (**c**) AANAP, adaptive As-Natural-As-Possible; (**d**) SPHP, shape-preserving half-projective warps; (**e**) our method. lighted, with blue boxes indicating parallax errors in the overlapping region and red boxes showing alignment errors at the junction of the overlapping and non-overlapping regions. The traditional SIFT algorithm exhibits poor alignment in the overlapping areas, as evident in both the stone lion and roof areas, which suffer from ghosting issues. The second row displays the results of the APAP algorithm, which effectively aligns the overlapping areas but still exhibits some ghosting problems.