## 4 Experiment
### Objective Evaluation
Table 3 shows the metric values of the enhanced images for the different methods in Figure 7. As can be seen from the table, our method achieves good results in each metric compared with the state-of-the-art direction. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **SRIE** & **JED** & **SDD** & **RetinexDIP** & **Ours** \\ \hline PSNR\(\uparrow\) & 13.4335 & 14.8575 & 14.1392 & 11.2798 & 22.2035 \\ \hline SSIM\(\uparrow\) & 0.5558 & 0.6847 & 0.6528 & 0.6019 & 0.8820 \\ \hline PI\(\downarrow\) & 5.1526 & 4.8825 & 4.7763 & 4.4450 & 3.7076 \\ \hline RMSE\(\downarrow\) & 69.3866 & 62.608 & 64.2121 & 67.2756 & 20.0939 \\ \hline NIQE\(\downarrow\) & 6.7049 & 5.9012 & 5.5274 & 7.0337 & 4.6071 \\ \hline BRISQUE\(\downarrow\) & 39.1997 & 23.4526 & 26.9602 & 28.6887 & 19.9940 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average performance of different enhancement methods in Figure 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **BIMEF** & **EFF** & **FFM** & **Ours** \\ \hline PSNR\(\uparrow\) & 13.1149 & 14.0921 & 12.7239 & 21.7908 \\ \hline SSIM\(\uparrow\) & 0.7410 & 0.7438 & 0.7104 & 0.8095 \\ \hline PI\(\downarrow\) & 2.8376 & 2.7542 & 2.9721 & 2.6137 \\ \hline RMSE\(\downarrow\) & 49.7947 & 47.0027 & 52.8759 & 41.9082 \\ \hline NIQE\(\downarrow\) & 3.4503 & 3.5421 & 3.5096 & 3.2326 \\ \hline BRISQUE\(\downarrow\) & 9.1046 & 9.5589 & 14.4858 & 13.8614 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average performance of different enhancement methods in Figure 6.  
In the performance test of the network, we quantitatively evaluated the LOL [1] dataset using six metrics, PSNR, SSIM, PI, RMSE, BRISQUE, and NIQE, and the evaluation results are shown in Table 4. The LOL dataset contains images that are rich in detail and complex. The performance of the EFF method and Zero-DCE++ method, in terms of PSNR and SSIM metrics, is ahead of the other methods. The GAN-network-based method EnlightGAN lacks the guidance of real labels in the learning process, so it does not perform as well as our method in terms of image quality. JED, FMM, and BIMEF use fusion strategies to enhance images but still cannot fully fuse global and local information, so the quality of enhanced images is poor and the metrics are not satisfactory. We use double attention to fuse the images, focusing on the balance of global and local, as can be seen from the table, our proposed method achieves the best performance on the LOL dataset, which also shows that our method has better results in enhancing the texture details of the images. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **PI\(\downarrow\)** & **RMSE\(\downarrow\)** & **NIQE\(\downarrow\)** & **BRISQUE\(\downarrow\)** \\ \hline SRIE (2016) & 14.7860 & 0.6014 & 5.0187 & 47.2893 & 6.5299 & 37.6744 \\ \hline BIMEF (2017) & 18.5550 & 0.7233 & 4.5758 & 35.9697 & 7.7284 & 30.0861 \\ \hline EFF (2017) & 20.4500 & 0.6193 & 5.1562 & 24.7017 & 9.2437 & 45.5565 \\ \hline RetinexNet (2018) & 14.5686 & 0.2990 & 5.2698 & 29.7274 & 9.4277 & 33.8642 \\ \hline JED (2018) & 18.0634 & 0.7725 & 4.5566 & 36.6428 & 5.7988 & 29.4081 \\ \hline FFM (2019) & 17.6911 & 0.7680 & 4.6958 & 36.4465 & 6.9621 & 28.7708 \\ \hline KinD (2019) & 16.5800 & 0.5765 & 3.5422 & 47.0003 & 4.9219 & 28.9662 \\ \hline EnGAN (2020) & 10.5615 & 0.6318 & 3.9861 & 39.7336 & 4.6520 & 20.4200 \\ \hline DLN (2020) & 17.9582 & 0.7383 & 3.7175 & 35.8579 & 6.2213 & 21.3218 \\ \hline Zero-DCE (2020) & 18.9855 & 0.6768 & 4.5768 & 33.9804 & 8.0578 & 30.1077 \\ \hline SDD (2020) & 17.1535 & 0.7259 & 4.2133 & 39.7718 & 5.3781 & 29.4991 \\ \hline DRBN (2020) & 14.6856 & 0.6011 & 4.0122 & 28.941 & 4.9865 & 28.9887 \\ \hline Zero-DCE++ (2021) & 20.0743 & 0.7903 & 3.9062 & 53.7578 & 6.4713 & 31.6658 \\ \hline RetinexDIP (2021) & 17.6561 & 0.6612 & 5.3600 & 31.9070 & 6.9657 & 23.2744 \\ \hline Ours & 25.066 & 0.8905 & 3.6612 & 16.5207 & 4.9034 & 18.7594 \\ \hline \hline \end{tabular}
\end{table}