# Dual-Branch Luminance-Chrominance Attention Network for Hydraulic Concrete Image Enhancement
## 4 Experiments and Results
### Results and Evaluation  
We trained the DBLCANet-HCIE model on the LOLV1 dataset and compared it with 12 other low-light image enhancement methods: CLAHE [9], LIME [52], KinD++ [53],  
Figure 8: Sample diagram of the LLHCID. URetinex-Net [54], HVI [55], LYT-NET [38], PPformer [36], Retinexformer [35], Retinex-Mamba [56], SALLIE [57], LCDBNet [1], and LLFormer [34]. Table 1 presents the quantitative performance of these diverse enhancement methods on the LOLv1 dataset. It is evident that our proposed method demonstrated superior performance compared to the majority of these methods in both the PSNR and SSIM indicators. Notably, our method achieved the best results in the SSIM category and the second-highest score in the PSNR metric, with a marginal difference of only 0.12 dB from the leading method, HVI. Figure 9 illustrates the results of qualitative comparisons between our proposed method and the existing approaches. The CLAHE method exhibited limited enhancement effects, while LIME improved the luminance but introduced severe distortion artifacts. Although KinD++ and LYT-Net achieved luminance enhancement, they simultaneously developed color over-saturation issues. Compared with the other enhancement methods illustrated in the images, our approach demonstrated better performance in terms of chromatic consistency, detail clarity, and luminance optimization. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **PSNR**\(\uparrow\) & **SSIM**\(\uparrow\) \\ \hline CLAHE [9] & 10.15 & 0.2872 \\ LIME [52] & 16.77 & 0.3974 \\ KinD++ [53] & 21.80 & 0.7912 \\ URetinex-Net [54] & 21.45 & 0.8169 \\ HVI [55] & 27.46 & 0.8413 \\ LYT-NET [38] & 24.13 & 0.8072 \\ PPformer [36] & 23.84 & 0.8317 \\ Retinexformer [35] & 25.16 & 0.8455 \\ RetinexMamba [56] & 24.03 & 0.8273 \\ SNA-Aware [57] & 24.61 & 0.8429 \\ LLFormer [34] & 23.65 & 0.8106 \\ LCDBNet [1] & 25.21 & **0.8442** \\ \hline DBLCANet-HCIE (Ours) & **27.34** & 0.8463 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative results on the LOLv1 dataset in terms of the PSNR and SSIM. For each metric, **bold** indicates the second-best result. Figure 9: Visual comparisons of our method and others on the LOLv1 dataset. As presented in Table 2, the PSNR and SSIM values of the seven models trained on the LOLv1 dataset showed a significant decrease when tested on our self-constructed LLHCID. Nonetheless, our method also achieved the highest scores in terms of the PSNR and SSIM. This indicates that the actual enhancement effects are far from satisfactory, which is further illustrated in Figure 10. The luminance enhancement is insufficient, and the detailed features are severely distorted. To ensure a fair and unbiased comparison, we retrained and evaluated all seven models on our self-constructed LLHCID under consistent experimental conditions. Specifically, the training data, preprocessing procedures, and key hyperparameter settings, such as \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **PSNR**\(\uparrow\) & **SSIM**\(\uparrow\) \\ \hline LYT-NET [38] & **16.58** & 0.7551 \\ Retinexformer [35] & 16.55 & 0.7295 \\ RetinexMamba [56] & 16.03 & 07364 \\ SNR-Aware [57] & 10.63 & 0.5531 \\ HVI [55] & 16.51 & **0.7916** \\ LCDBNet [1] & 15.49 & 0.7376 \\ \hline DBLCANet-HCIE (Ours) & 16.77 & 0.8022 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative results for models trained on the LOLV1 dataset and tested on the LLHCID in terms of PSNR and SSIM. For each metric, **bold** indicates the second-best result. Figure 10: Visual comparisons of direct testing on the LLHCID with models trained on the LOLv1 dataset. learning rate, batch size, optimizer, and number of epochs, were maintained across all models. For the baseline models, we adopted their publicly available official implementations and retained their default hyperparameter settings, which were established by the original authors.