## 3 Results
### Quantitative SIFT Analysis and Statistical Validation  
With the objective of evaluating the success of the proposed method in structural preservation, the Scale-Invariant Feature Transform (SIFT) algorithm, which is insensitive to scale and orientation changes and provides rotation-invariant feature extraction, was  
Figure 2: Visual comparison of enhancement results for selected underwater images from the Test-C60 dataset. Each row corresponds to a different input image type (e.g., greenish, bluish, yellowish), while each column shows the result obtained by a different enhancement method, including the proposed method [11]. used on all methods. SIFT objectively measures the structural similarity of the enhanced images to the original raw images by detecting characteristic edges, corners and structure points in the images. In this direction, the number of matching key points detected between the enhanced images and the original images for each method was calculated and the average scores are presented in Table 6. Furthermore, the statistical significance of the structural differences between the methods was analyzed with the Wilcoxon signed-rank test, and the results are given in Table 7. The high number of SIFT matches indicates the success of the method in preserving details and improving structural integrity; however, the reliability of this increase should be supported by evaluating it together with quality metrics such as IL-NIQE or AG. The proposed method outperforms all competitors with an average of 2923 matches on the Test-C60 dataset, increasing the structural detail saliency by approximately 3.9 times compared to the input image. It also outperforms its closest follower, [PERSON] et al. 's method, by 7% and classical methods such as IBLA and HP by 30-50%. On the OceanDark dataset, a 46% increase is achieved compared to the baseline input with an average of 6122 matches. This high match rate leaves [PERSON] et al. 's, HFM, and other color-based enhancement methods behind; it ranks second only to UTV's extremely high number of matches (8470). However, when this extraordinary increase in UTV on OceanDark is evaluated together with the  
Figure 3: Visual comparison of enhancement results for selected underwater images from the OceanDark dataset. Each row corresponds to a different input image type (e.g., hazy, low-contrast), while each column shows the result obtained by a different enhancement method, including the proposed method [11]. serious deterioration in the IL-NIQE score (38.82), it is understood that a significant portion of the recorded matches are due to noise. The SIFT-based Wilcoxon signed-rank test results presented in Table 7 show that the proposed method provides statistically significant superiority in terms of preserving structural details in both the Test-C60 and OceanDark datasets. When compared against all methods in Test-C60, significant differences were obtained at the level of \(p<0.01\), and no significance was observed only against [PERSON] et al. 's method (\(p=0.388\)). In the OceanDark dataset, extremely strong differences were detected, especially against methods such as UT, FUnIEGAN and HFM at the level of \(p<10^{-30}\). The SIFT-based analyses performed revealed that the proposed method preserved structural details with the highest accuracy in both the Test-C60 and OceanDark datasets. Although no statistically significant difference \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Test-C60** & **OceanDark** \\ \cline{2-3}  & **SIFT** & **SIFT** \\ \hline Input & 748 & 4180 \\ IBLA & 1964 & 5337 \\ CBF & 1691 & 4458 \\ WCD & 977 & 5778 \\ ULAP & 1256 & 5837 \\ RGHS & 1992 & 5226 \\ HP & 1957 & 4582 \\ UTV & 1048 & 8470 \\ UDCP & 1046 & 5553 \\ FUnIEGAN & 152 & 588 \\ TOPAL & 217 & 789 \\ USUIR & 1753 & 4811 \\ UT & 200 & 525 \\
[11] & 2739 & 5989 \\ HFM & 2165 & 5677 \\ Proposed & 2923 & 6122 \\ \hline \hline \end{tabular}
\end{table}