# Dual-Branch Luminance-Chrominance Attention Network for Hydraulic Concrete Image Enhancement
## 4 Experiments and Results
### Results and Evaluation
The results of the quantitative comparison of the LLHCID test set are shown in Table 3, where our proposed method exhibits excellent performance in terms of the PSNR metric, achieving 28.61 dB. Our method outperforms the second-best approach, Retinexformer [35], by 1.48 dB in PSNR. Compared to the other competing methods, including LYT-Net [38], RetinexMamba [56], SNR-Aware [57], HVI [55], and LCDBNet [1], our approach achieves improvements of 4.96 dB, 1.87 dB, 3.81 dB, 1.98 dB, and 2.05 dB, respectively. In terms of SSIM, which reflects structural similarity and perceptual fidelity, our method achieves the highest score of 0.9574, outperforming LYT-Net by 0.1022 and showing improvements of 0.0361, 0.0473, 0.0498, 0.0298, and 0.0215 over Retinexformer, RetinexMamba, SNR-Aware, HVI, and LCDBNet, respectively. These results clearly demonstrate the superior ability of our DBLCANet-HCIE to enhance low-light hydraulic concrete images while effectively preserving structural and texture details. To comprehensively evaluate the computational efficiency and complexity of the proposed model, we compared seven low-light image enhancement methods in terms of floating-point operations (FLOPs), number of parameters, and average inference time per image. To ensure fairness and comparability, all models were evaluated in the same hardware environment, with input tensors uniformly set to a size of 3 \(\times\) 400 \(\times\) 600, consistent with the training dataset. For inference time evaluation, each model was run multiple times, and the average value was recorded to reduce the impact of random fluctuations. As shown in Table 3, DBLCANet-HCIE contains 2.13 million parameters and requires 63.90 GFLOPs, achieving the best performance among all compared approaches while maintaining a moderate level of model complexity. Figure 11 presents a visual comparison of the results after training on the LLHCID, clearly illustrating that the images enhanced using our proposed method most closely resembled the ground-truth images. The HVI, LYT-Net, and Retinexformer methods exhibited certain distortions in color representation, while the RetinexMamba, LCDBNet, and SNR-Aware methods showed less than ideal performance in terms of detail preservation. The DBLCANet-HCIE method exhibited the best performance among the seven methods in terms of its luminance enhancement and detailed feature preservation. The defect edge details were also well preserved. \begin{table}
\begin{tabular}{c c c c c} \hline
**Methods** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **Params (M) \(\downarrow\)** & **FLOPS (G) \(\downarrow\)** \\ \hline LYT-NET [38] & 23.65 & 0.8552 & 0.045 & 16.66 \\ Retinexformer [35] & **27.13** & 0.9213 & **1.61** & 62.32 \\ RetinexMamba [56] & 26.74 & 0.9101 & 3.59 & 139.10 \\ SNR-Aware [57] & 24.80 & 0.9076 & 3.91 & **52.70** \\ HVI [55] & 26.63 & 0.9276 & 1.98 & 59.00 \\ LCDBNet [1] & 26.56 & **0.9359** & 7.57 & 131.96 \\ DBLCANet-HCIE (Ours) & 28.61 & 0.9574 & 2.13 & 63.90 \\ \hline \end{tabular}
\end{table} 
Table 3: Quantitative results on the LLHCID in terms of the PSNR and SSIM. For each metric, bold indicates the second-best result. Comparisons of the enlarged defect regions in the visual results reveal that RetinexMamba, LCDBNet, and SNR-Aware introduce noticeable noise and artifacts around defect areas, which significantly compromise visual quality. Although LYT-Net, HVI, and Retinexformer show improved performance in suppressing such artifacts, they still suffer from detail loss and substantial color distortion in defect regions. In contrast, the proposed method not only enhances overall luminance effectively but also preserves fine-grained defect textures and removes noise, producing visual results that are visually closer to the reference bright-light images.