## 4 Experiment
### Ablation Experiments  
The EFCNet method enhances the image by using cross-attention fusion units in a progressive iterative manner. Therefore, we perform ablation experiments to find the optimal number of iterations. We use 1-7 iterations of cross-attention fusion units to train the model. The results of the enhanced images obtained with different number of iterations are shown in Figure 13. It can be observed that when the number of iterations is one, the clarity of the enhanced image is relatively poor. When the number of iterations is two, the hue information of the image is inaccurate, the image becomes green, and the objects in the scene are blurred. When the number of iterations is three, the enhancement effect is obviously improved, but the scene has more artifacts. We increased the number of iterations to four, five, six, and seven, and we found that the image contrast and brightness reached the best when the number of iterations was four. In addition, we objectively evaluated the set of enhancement results for a different number of iterations using the BRISQUE and NIQE metrics, and Table 7 shows the metric values for a different number of iterations. Smaller values of BRISQUE and NIQE metrics indicate better results. As can be seen from the table, the results after four iterations have the lowest values in BRISQE and NIQE. Therefore, we determined that the optimal number of iterations is four. In order to obtain a clearer picture of the performance of each module, we perform four sets of ablation experiments on the network model. To obtain a clear picture of the performance of each part of the network, we performed ablation experiments. In the ablation experiments, 1500 images from RetinexNet [1] were used for training. The image size, learning speed, epoch, and number of recursions were the same for the four sets of ablation experiments. We randomly selected three images from the DICM and MEF datasets to compare the visual effects of the enhanced images from different ablation experiments, which are shown in Figure 14. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**BRISQUE**} & \multicolumn{6}{c}{**NIQE**} \\ \hline
**Method** & **MEF** & **DICM** & **VV** & **NPE** & **LIME** & **MEF** & **DICM** & **VV** & **NPE** & **LIME** \\ \hline SRIE (2016) & 31.6755 & 31.0909 & 30.0013 & 28.8648 & 21.3854 & 9.8002 & 4.4626 & 4.2338 & 4.0076 & 4.3107 \\ BIMEF (2017) & 20.5770 & 24.2961 & 23.1839 & 21.9251 & 13.7832 & 4.3061 & 3.8263 & 3.6850 & 3.6893 & 4.8900 \\ EFF (2017) & 33.3979 & 34.7787 & 35.0649 & 33.0947 & 32.7853 & 4.6912 & 3.3572 & 3.6916 & 3.4547 & 3.9745 \\ RetinexNet (2018) & 24.9352 & 27.5574 & 25.8767 & 25.1027 & 16.9106 & 6.1001 & 4.4492 & 4.1033 & 4.2984 & 5.4387 \\ JED (2018) & 25.1958 & 28.3969 & 29.3638 & 28.5549 & 15.4578 & 6.4179 & 3.9760 & 4.8492 & 3.8585 & 6.1167 \\ FFM (2019) & 25.5495 & 25.6837 & 29.4840 & 23.6145 & 13.8984 & 4.7857 & 3.7660 & 3.5581 & 3.7240 & 4.9322 \\ KinD (2019) & 25.9532 & 24.9554 & 24.5015 & 23.2977 & 14.8946 & 5.6242 & 4.1372 & 3.5892 & 3.7670 & 6.6546 \\ EnGAN (2020) & 14.3043 & 14.3979 & 19.5189 & 22.4630 & 12.8414 & 3.2211 & 3.1189 & 3.3489 & 3.8172 & 3.3794 \\ DLN (2020) & 22.3135 & 23.2821 & 25.8513 & 23.6826 & 13.9512 & 3.3556 & 4.0048 & 3.6935 & 3.8892 & 4.09 \\ Zero-DCE (2020) & 24.545 & 24.8596 & 22.7712 & 24.0938 & 13.7468 & 4.4834 & 4.5601 & 3.5707 & 3.8097 & 4.6631 \\ SDD (2020) & 28.3008 & 27.8422 & 27.4322 & 27.1678 & 15.0549 & 6.1955 & 3.8403 & 4.3646 & 3.7466 & 5.5152 \\ DRBN (2020) & 26.6506 & 18.7275 & 18.2681 & 17.3514 & 18.2028 & 5.7662 & 3.8799 & 3.9150 & 3.7202 & 4.6236 \\ Zero-DCE++ (2021) & 17.8427 & 20.3962 & 22.3832 & 20.6055 & 19.9268 & 3.3954 & 3.5376 & 3.6212 & 3.7690 & 3.9949 \\ RetinexDIP (2021) & 16.4742 & 19.5039 & 21.7850 & 19.9879 & 18.3420 & 3.2827 & 3.7192 & 3.5898 & 3.5473 & 3.7112 \\ Ours & 11.6015 & 8.2625 & 13.0735 & 13.8618 & 12.5453 & 3.0018 & 3.07 & 3.3356 & 3.4629 & 3.0663 \\ \hline \hline \end{tabular}
\end{table}