## 4 Underwater Image-Enhancement Experiments and Result Analysis
### Quantitative Evaluation  
Reference-free objective metrics were used to eliminate subjective bias in assessing the enhancement quality. Three indices--the UIQM, AG, and EN--were chosen to measure color fidelity, sharpness, and contrast, respectively. Table 1 lists the scores obtained by the seven algorithms for the eight test images in Figure 3; the highest value for each metric is shown in the background shading. The results show that the WWSF method achieves the best performance in both UIQM and AG, indicating its significant advantages in color restoration, edge sharpness, and detail preservation. This can be attributed to its wavelet-based fusion strategy combined with Sobel-weighted enhancement, which effectively strengthens structural features while suppressing noise and redundant textures. In contrast, the WWPF method achieves the highest EN score, reflecting its effectiveness in increasing the overall information entropy of the image. However, it is worth noting that an excessively high EN value may introduce redundant textures and noise, which can compromise the structural clarity and consistency. By comparison, WWSF deliberately moderates EN to prioritize structural integrity and visual purity, especially beneficial in underwater structural imagery. Figure 4a-c further visualize the metric trends. It is evident that WWSF significantly outperforms competing methods in both UIQM and AG, while maintaining competitive EN values--demonstrating a well-balanced trade-off between enhancement and structural coherence. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **Metric** & **a** & **b** & **c** & **d** & **e** & **f** & **g** & **h** \\ \hline \multirow{4}{*}{CLAHE} & UIQM & 1.012 & 1.081 & 1.098 & 0.98 & 0.884 & 1.139 & 0.841 & 0.88 \\  & AG & 3.279 & 3.259 & 3.895 & 3.428 & 4.886 & 3.982 & 3.677 & 4.306 \\  & Entropy & 6.757 & 6.91 & 7.126 & 7.688 & 7.235 & 7.142 & 6.972 & 7.69 \\  & UIQM & 0.533 & 0.631 & 0.434 & 0.557 & 0.791 & 0.729 & 0.611 & 0.88 \\  & AG & 3.913 & 3.169 & 3.899 & 2.697 & 4.855 & 4.133 & 3.892 & 3.043 \\  & Entropy & 7.569 & 7.522 & 7.312 & 7.787 & 7.779 & 7.413 & 7.528 & 7.787 \\  & UIQM & 1.019 & 0.957 & 1.395 & 0.831 & 1.347 & 1.138 & 1.254 & 1.007 \\  & AG & 5.727 & 5.048 & 5.887 & 4.538 & 7.061 & 7.097 & 5.429 & 4.949 \\  & Entropy & 7.731 & 7.755 & 7.656 & 7.913 & 7.714 & 7.74 & 7.575 & 7.905 \\  & UIQM & 1.123 & 0.894 & 1.030 & 0.72 & 0.826 & 1.055 & 0.867 & 0.776 \\  & AG & 4.719 & 3.68 & 4.365 & 3.023 & 5.083 & 5.062 & 3.949 & 3.629 \\  & Entropy & 7.813 & 7.719 & 7.489 & 7.773 & 7.833 & 7.675 & 7.519 & 7.862 \\  & UIQM & 0.303 & 0.733 & 0.358 & 0.103 & 0.373 & 0.636 & 0.362 & 0.387 \\  & AG & 3.567 & 2.334 & 3.849 & 3.671 & 4.614 & 3.567 & 4.141 & 3.812 \\  & Entropy & 6.853 & 6.19 & 6.856 & 7.266 & 7.236 & 6.963 & 6.937 & 7.340 \\  & UIQM & 0.999 & 0.917 & 1.307 & 0.827 & 1.335 & 1.056 & 1.179 & 1.006 \\ MLLE & AG & 5.930 & 5.236 & 6.112 & 4.419 & 7.700 & 7.003 & 5.620 & 5.122 \\  & Entropy & 7.310 & 7.442 & 7.327 & 7.706 & 7.639 & 7.487 & 7.294 & 7.706 \\  & UIQM & 1.443 & 1.391 & 1.433 & 1.187 & 1.463 & 1.395 & 1.319 & 1.220 \\ WWSF & AG & 12.474 & 9.018 & 9.921 & 8.731 & 15.510 & 10.504 & 9.971 & 8.524 \\  & Entropy & 7.358 & 7.344 & 7.488 & 7.590 & 7.721 & 7.435 & 7.445 & 7.574 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation of multiple underwater-image-enhancement algorithms. Figure 3: Comparison of the original image and images enhanced by different algorithms. In summary, WWSF not only excels across multiple objective metrics but also aligns well with qualitative visual assessments, confirming its effectiveness and practical value for underwater structural inspection tasks. Beyond visual improvement, the method provides clearer and more consistent image inputs for both manual interpretation and automated detection models, making it a promising choice for real-world engineering applications.