## 3 Experiments and Analysis
### Evaluation Metrics  
To validate the image enhancement quality achieved by the method proposed in this paper, we employed several objective evaluation metrics [45] to assess the performance of our underwater image enhancement technique. Firstly, we utilize the mean square error (MSE) to calculate the average squared difference between the pixels of the enhanced underwater image and the reference image. The MSE provides a quantitative measure of the overall distortion between the enhanced image and the reference image, with a lower MSE value indicating a better image quality. The second metric employed is the peak signal-to-noise ratio (PSNR), commonly used to evaluate the signal reconstruction quality in domains such as image compression. The PSNR measures the ratio between the peak signal and the noise level and serves as an indicator of the extent of distortion in the processed underwater image compared to the reference image. Higher PSNR values indicate lower levels of distortion. To assess the visual quality of the enhanced image, we utilized the structural similarity index (SSIM). The SSIM evaluates the similarity between the enhanced image and the reference image based on key features, including luminance, contrast, and structural information. Higher SSIM values indicate a higher degree of similarity between the enhanced image and the reference image, reflecting an improved visual quality.  
Furthermore, we calculated the average underwater image quality measure [46] (UIQM) for the quantitatively enhanced images. The UIQM is a metric that takes into account the degradation mechanism and optical imaging properties specific to underwater images. This metric comprises three distinct components: the underwater image color metric (UICM), the underwater image sharpness metric (UISM), and the underwater image contrast metric (UIConM). These metrics, respectively, evaluate the color fidelity, sharpness, and contrast of the underwater images. Each attribute metric can be utilized independently to assess a specific aspect of underwater image degradation. Higher scores in the UIQM indicate a more perceptually consistent outcome in line with human visual perception. The expression for the UIQM is as follows:  
\[\text{UIQM}=c_{1}\times\text{UICM}+c_{2}\times\text{UISM}+c_{3}\times\text{ UIConM} \tag{12}\]  
where the color, sharpness, and contrast of the image are represented as linear combinations, with respective weighting coefficients of \(c_{1}\) = 0.0282, \(c_{2}\) = 0.2953, and \(c_{3}\) = 3.5753. The weight values utilized in this study are derived from [46].  
In order to validate the superiority of our proposed method in terms of computational requirements and real-time performance, we evaluate the efficiency of the model based on the number of model parameters, GFLOPs, and the single image processing time. These metrics provide insights into the computational demands of the algorithm. Notably, the number of parameters serves as a key indicator of model complexity, whereby a higher parameter count indicates greater computational resource and data requirements for model training and execution. GFLOPs, on the other hand, quantify the number of floating-point operations necessary to execute a network model once, thus serving as a measure of computational efficiency and speed. Furthermore, the single image processing time directly signifies the algorithm's efficiency in handling individual images.