## 5 Discussion  
The proposed algorithm excels in both subjective and objective assessments, demonstrating marked improvements in overall clarity, as well as in fine-texture rendering for hydraulic infrastructure inspection images. Its success stems from a structure-aware, WWSF framework that highlights defect edges while suppressing noise. The preceding steps--MSRCR color correction, \(\gamma\)-adjustment, and CLAHE-based contrast enhancement--provide balanced luminance and chromatic information, which the fusion stage can then exploit.  
Figure 5: Images of different ablation studies: (**a**) Original image; (**b**) Image without MSRCR method; (**c**) Image without gamma-correction method; (**d**) Image without CLAHE method; (**e**) Image using normal fusion; (**f**) WWSF (full model).  
Unlike most current enhancement studies--which center on de-hazing, global clarity, color restitution, or contrast--this work targets the texture detail essential for recognizing cracks, corrosion, spalling, and voids in hydraulic facilities. Conventional underwater algorithms often perform poorly under artificial lighting: dark-channel priors (e.g., GDCP, UDCP) misestimate tones and introduce color shifts [31, 40]; CLAHE boosts contrast but leaves textures muted; and classic gray-world white balance fails on near-monochrome concrete and metal surfaces. In contrast, MSRCR, grounded in Retinex theory, operates on local reflectance differences, recovering both color constancy and structural cues vital to condition assessment.  
Texture enhancement is crucial because subsequent tasks--segmentation and defect recognition--depend on accurate structural cues. Physical-model approaches cannot resolve this, and standard fusion rules (averaging, max-selection) ignore the complexity of underwater scenes. Decomposing images with the discrete wavelet transform and weighting sub-bands using the Sobel structure energy preserves orientation-specific edges while attenuating background noise, outperforming average-gradient weights in both detail fidelity and artifact control. Experiments confirm that Sobel-weighted fusion not only heightens clarity and contrast but also reduces noise and color error, delivering reliable inputs for downstream analysis.  
WWSF surpasses pyramid-based and unweighted fusion in visual sharpness and structural detail, yet its EN scores lag behind those of WWPF and RGHS. This drop suggests that some high-frequency coefficients were intentionally suppressed to maintain structural consistency and visual purity. Future work should aim to strike a better balance between detail richness and noise control--potentially through deep learning-driven adaptive weighting schemes--to improve information density without compromising clarity. We also plan to conduct comparative experiments with representative deep learning-based enhancement models such as WaterNet and UIE-CNN, which have demonstrated strong capabilities in addressing non-uniform illumination and severe scattering through data-driven learning. Benchmarking the proposed WWSF framework against these approaches will not only further validate its performance but may also inspire future hybrid enhancement strategies that combine traditional fusion mechanisms with deep neural networks.