## 2 Literature Review  
Recent studies have shown that applying deep learning models following initial image enhancement preprocessing can significantly improve the accuracy of target detection tasks. In underwater inspection scenarios, image quality is often degraded by factors such as wavelength-dependent light absorption, scattering, and non-uniform illumination, making image enhancement a critical prerequisite for restoring key structural information. For example, [PERSON] et al. demonstrated that traditional image augmentation techniques--such as brightness adjustment, scaling, and simulated weather effects--can effectively enrich image datasets and improve window state recognition performance using Faster R-CNN [13]. Similarly, [PERSON] et al. found that image enhancement significantly improves the robustness and accuracy of non-protective equipment detection based on YOLOv8 under visually degraded conditions [14]. However, their experiments also revealed that not all enhancement strategies are universally effective; the improvement in model performance depends on the specific detection task. Therefore, enhancement strategies should be tailored to the characteristics and types of the target objects. In other words, to improve the defect detection performance of hydraulic structures in underwater imagery using deep learning models, it is necessary to develop image enhancement methods suitedto the specific types of structural defects. These findings further emphasize the importance of image enhancement in underwater inspection tasks, not only for optimizing datasets but also for enhancing the performance of subsequent target recognition.  
Existing enhancement algorithms can be grouped into three broad families: (i) Non-model-based adjustment methods--for example, global or adaptive histogram equalization [15], contrast-limited adaptive histogram equalization (CLAHE) [16], white-balance heuristics [17], and Retinex-type operators [18]--redistribute pixel intensities without explicit regard for underwater optics; their performance deteriorates in turbid or unevenly lit scenes [19; 20]. (ii) Physics-model-based restoration methods estimate the parameters of an underwater imaging model--typified by the underwater dark channel prior (UDCP) [21] and the Sea-thru framework [22]--but depend heavily on accurate scene assumptions and depth priors. (iii) Fusion-based strategies combine complementary pre-enhancements via multiscale pyramids [23] or wavelet decomposition [24] to exploit their respective advantages. The concept of wavelet decomposition was first introduced in the early 1980s [25], and has become a cornerstone in the fields of signal and image processing. However, many fusion strategies still fail to preserve fine structural features in these specific applications.  
To address these limitations, our WWSF algorithm is proposed. The principal contributions are the following:  
* Parallel a priori enhancement paths are designed, and a structure-guided fusion mechanism is embedded to accentuate crack edges and scour boundaries.
* A Sobel-energy weighting scheme is introduced that assigns higher fusion weights to regions rich in gradient energy, thereby preserving high-detail content.
* Quantitative metrics and qualitative evaluations are conducted on real-world datasets of hydraulic infrastructure, demonstrating superior enhancement quality and structural fidelity compared to state-of-the-art methods.