## 4 Experiment
### Objective Evaluation  
In Section 4.2, we subjectively analyze the enhancement effects of other methods and the method in this paper. The subjective analysis is relatively intuitive, but sufficient statistical data are still needed to illustrate the superiority of the method in this paper. Therefore, we use objective metric data for quantitative analysis of image quality. First, we measure the performance of the model using objective metrics for each of the methods in Figures 5-7. Then, we objectively evaluate the performance of each method on the complete datasets of LOL [1], MIT [32], NPE [37], MEF [34], VV [36], DICM [35], and LIME [38]. Finally, we perform an objective analysis of the objectively evaluated values. For the dataset with authentic labels, LOL [1] and MIT [32] use six metrics PSNR, SSIM [42], BRISQUE [43], NIQE [44], PI [45], and RMSE [45]. For the datasets MEF [34],VV [36], DICM [35], NPE [37], and LIME [38] without real labels, we used the no-reference indicators BRISQUE [43] and NIQE [44]. PSNR is based on the image pixel statistics, which calculates the difference between the grayscale value of the image and the corresponding pixel of the real label. The larger the PSNR value is, the better the noise immunity of the evaluated image is and the better the image quality is. SSIM is based on structural information and measures the structural similarity between the evaluated image and the real label image from three perspectives: brightness, contrast, and structure, respectively; larger SSIM values indicate better image recovery and lower distortion. The PI value represents the subjective perceptual quality of an image, the lower the PI value, the more realistic the image looks, and the lower the PI value, the better the perceptual quality of the image. Figure 11: Comparison of the visual effects of the enhanced indoor images in the DICM [35] dataset. Figure 12: Comparison of the visual effect of the enhanced night view images in the LIME [38] dataset. The RMSE value measures the deviation between the algorithm score value and the subjective score value, and the smaller the value, the better the performance of the algorithm. BRISQUE is a reference-free spatial domain image quality assessment metric, which extracts the contrast normalization coefficients from the image, extracts the fitted Gaussian distribution features, and inputs to SVM to perform regression to obtain the image quality assessment results. The smaller the value of BRISQUE, the better the image quality recovery. NIQE is also a reference-free image quality assessment metric, which uses a natural scene statistical model and a multivariate Gaussian model to calculate the feature distance of the image. A smaller value of NIQE indicates a higher quality of the network-enhanced image. Table 1 shows the metric values of the enhanced images for the different methods in Figure 5. Our method and the JED method achieve better results for PSNR and SSIM metrics, which indicates that the enhanced images contain more detailed content and structural information. Compared with the second best metrics, our PI, RMSE, NIQE, and BRISQUE are reduced by 0.7374, 42.5141, 0.9203, and 3.4586, respectively. Table 2 shows the metric values of the enhanced images for the different methods in Figure 6. The EFF method ranks second in terms of performance in the PSNR, SSIM, PI, and RMSE metrics. In the NIQE and BRISQUE metrics, the performance of the BIMEF method is ranked second. EFF studies the relationship between two maps with different exposure levels using illumination estimation to estimate the exposure rate map and adjusts each pixel to normal exposure according to the estimated exposure rate map. BIMEF [14] is a low-light image enhancement algorithm based on Multiple Exposure Fusion(MEF). Unlike EFF, for BIMEF, we use cross-fusion attention to fuse two images, fusing from global and local features. Compared with EFF and BIMEF, our method achieves the best results in all six metrics.