## 1 Introduction  
The global development of hydraulic infrastructure is on the rise. The 2024 release of the International Commission on Large Dams (ICOLD) World Register of Dams lists more than 62,000 large dams [1,2], a figure that has grown steadily over the past decade. Regular inspection, therefore, represents an increasingly important element of global water-safety governance [3].  
Long-serving underwater components--piers, stilling basins, draft-tube linings--are exposed to continuous immersion, chemical corrosion, abrasive sediment transport, and cyclic hydrodynamic loading [4]. These mechanisms accelerate surface scaling, reinforcement exposure, and ultimately the loss of structural capacity, escalating the risk of service failures or catastrophic collapse. Targeted, high-resolution visual assessment is thus indispensable for validating stability and informing maintenance programs.  
Remotely operated vehicles (ROVs) equipped with optical cameras currently provide the primary data stream for apparent damage detection [5; 6; 7]. Unfortunately, raw underwater images suffer from backscatter veiling, wavelength-dependent absorption, severe scattering, and non-uniform ambient illumination, leading to color casts, low contrast, and blurred details. Such degradations impede human interpretation and automated defect recognition [8; 9]. Backscatter, scattering, and blur obscure key details like cracks and exposed reinforcements, making human inspection unreliable. Color distortion can hide signs of corrosion, while low contrast reduces visibility. For automated detection, these degradations confuse features and lower model accuracy. Deep learning models rely on clear textures and edges--poor image quality results in suboptimal detection performance. Thus, image enhancement is essential for both human and machine-based underwater inspections. [PERSON] et al. [10] noted that poor underwater visibility limited the visual verification of detected faults during underwater pipeline inspection. Image enhancement methods can effectively address this issue by improving the clarity of underwater imagery and supporting both manual and model-based diagnosis.  
This paper introduces a wavelet fusion algorithm with Sobel-based weighting (WWSF), tailored for the underwater inspection of hydraulic structures. The WWSF algorithm performs wavelet fusion with weights derived from the edge response intensity. The pipeline comprises (1) color correction using multiscale Retinex with color restoration (MSRCR) [11]; (2) gamma-guided luminance compensation in the CIELAB color space, with CLAHE applied to the L channel [12]; (3) the multilevel discrete wavelet decomposition of both pre-enhanced outputs; and (4) the Sobel-guided weighting of high-frequency sub-bands, followed by inverse fusion to yield the final image.  
The remainder of the paper is organized as follows. Section 2 presents a review of the relevant literature. Section 3 details the WWSF algorithm and its implementation. Section 4 reports the experimental settings and results. Section 5 discusses the comparative performance and outlines future research directions, including physics-informed deep priors for real-time inspection. Section 6 summarizes the main conclusions.