# An Improved SIFT Underwater Image Stitching Method
## 4 Results
### Quantitative Comparisons  
#### 4.2.1 Comparison with the Traditional SIFT Algorithm  
Our method demonstrates remarkable robustness when compared to the traditional SIFT algorithm, especially in scenarios where the textures surrounding the features exhibit high similarity. Figure 5 presents a visual comparison of the matching outcomes between our method and the conventional SIFT algorithm. The colored lines represent feature matching between two images, while the red boxes represent better stitching images obtained by our algorithm compared to traditional SIFT algorithms. Image A refers to the reference image and image B refers to the training image. As evident in the figure, the traditional SIFT algorithm struggled to obtain a satisfactory number of feature matches in the experimental image. Out of the 133 detected matching pairs, only 73 were accurate, resulting in an accuracy rate of 54.89%. This inaccuracy leads to improper alignment and significant ghosting artifacts in the traditional SIFT algorithm's results. Similar to the traditional SIFT algorithm, the outcomes of other stitching methods are also suboptimal. In contrast, our algorithm utilizes the projected transformed image for precise feature extraction and matching. This approach resulted in the identification of 85 correct matching pairs out of 139 detected, corresponding to an accuracy rate of 61.15%. Consequently, our algorithm excels in detecting a greater number of feature pairs and achieving higher matching accuracy, even in cases where feature detection is challenging in underwater images. The stitching results further underscore the superior alignment performance of our algorithm.  
Figure 6 illustrates the comparative matching results between our method and the conventional SIFT algorithm using a different set of images. The colored lines represent feature matching between two images, while the red boxes represent better stitching images obtained by our algorithm compared to traditional SIFT algorithms. Image A refers to the reference image and image B refers to the training image. As depicted in Figure 6, the traditional SIFT algorithm successfully identified 277 correct matches out of 430 pairs, achieving an accuracy rate of 64.42%. In contrast, our algorithm identified 304 correct matches out of 434 pairs, yielding an accuracy rate of 70.05%. The results highlighted within the red box emphasize the limitations of the traditional SIFT algorithm, which excels in matching regions with distinct texture features but falters in regions with less prominent textures. Conversely, our algorithm exhibits the capability to accurately detect feature matches in regions with both prominent feature points and areas where the image contours are less distinct, leading to an enhanced quantity and accuracy of matches. This experiment underscores the heightened robustness and improved matching performance of our approach, stemming from our precise feature extraction and matching methodology.  
Figure 5: An image feature matching case: (**a**) Image inputs to be stitched. (**b**) Our feature matching and the traditional SIFT algorithm. (**c**) Stitching results of the traditional SIFT algorithm and ours.