## 4 Experiment
### Subjective Evaluation
Both RetinexNet and KinD methods are convolutional neural networks proposed based on Retinex theory, and both methods effectively improve the brightness of images.The enhancement process of RetinexNet and KinD methods only enhances the light component, causing image color inaccurate information and vignetting of object edges, such as the second blue dress in Figure 7b, which causes the image to look blurred because the vividness is excessively increased. The lawn in the fourth picture of Figure 7c completely loses its original green color. Figure 7d is the result of EnGAN enhancement  
Figure 5: Example of comparison with Retinex-theory-based low-light image enhancement method on the LOL [1] dataset. Figure 6: Example of comparison with a low-light image enhancement method based on exposure image fusion on the SICE (Single Image Contrast Enhancer) [33] dataset. by the weakly supervised network, and the brightness and color of the image are more balanced. However, the sharpness of the image needs to be improved. Figure 7e is the enhancement result of DLN, and the overall effect of the image is better, but white color appears in the sky in the fifth image in Figure 7e. The brightness of the images enhanced by the methods in Figure 7g,h is still relatively dark. Both Zero-DCE and Zero-DCE++ are a kind of unsupervised learning neural network. As a whole, the enhanced images of the Zero-DCE method have uniform colors, but the enhanced image regions still have dark areas due to the lack of guidance from the real labels during the network learning process. However, the enhanced results of Zero-DCE++ appear overexposed in some regions. Figure 7 shows the enhancement results of the method in this paper, and it can be seen that the outline of the object is clear and the details are complete. Figure 8 shows the enhancement effects of different enhancement methods on the LOL dataset. We randomly selected 2 images from the LOL dataset to compare the visual  
Figure 7: Example of comparison with CNN-based low-light image enhancement methods on the MIT [32] dataset. effects of the 14 methods. In addition, some regions are selected for magnification display. From the figure, we can observe that the images enhanced by SRIE and SDD are not sufficiently exposed during the enhancement process, resulting in low brightness of the whole image and obvious dark areas. For example, the brightness of the bowling ball area and the face of the person enhanced by SDD is very low. The RetinexNet method causes the image to be severely sharpened, and at the same time, the image appears with chromatic aberration and a lot of noise. However, the images enhanced by JED, BIMEF, DRBN, and KinD have a more uniform luminance and color distribution from the overall view, but there is still noise in the images. The DLN method causes the face of the person to be overexposed. The results of RetinexNet and RetinexDIP enhancement have a lot of noise. The unsupervised methods Zero-DCE and Zero-DCE++ enhance the results with uniform luminance, but the images have uneven tones. In general, compared with the real image, the enhanced image of our proposed method has appropriate brightness, no obvious dark areas, rich and natural color without distortion, and better preservation of image detail information. Figure 9 shows the enhancement effect of different enhancement methods on the cloudy weather images in the NPE dataset. In life, it can be observed that the sky in rainy weather appears gray, and it is very important to keep the hue of this particular scene of rainy weather during image enhancement. The EnGAN, DLN, and DRBN methods can be observed in the presence of overexposed areas, causing severe loss of image information, such as dark clouds in the sky being enhanced to white or light green. The brightness of the middle part of the SRIE-enhanced image is uniform, but black artifacts exist in the bottom edge region of the image enhanced by JED, RetinexDIP, KinD, and RetinexNet.